3026 lines
127 KiB
Markdown
3026 lines
127 KiB
Markdown
title: libigl Tutorial
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author: Daniele Panozzo and Alec Jacobson
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date: 07 November 2015
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css: style.css
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# libigl tutorial notes
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#### as presented by Daniele Panozzo and Alec Jacobson at SGP Graduate School 2015
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Libigl is an open source C++ library for geometry processing research and
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development. Dropping the heavy data structures of tradition geometry
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libraries, libigl is a simple header-only library of encapsulated functions.
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This combines the rapid prototyping familiar to Matlab or Python programmers
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with the performance and versatility of C++. The tutorial is a self-contained,
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hands-on introduction to libigl. Via interactive, step-by-step examples, we
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demonstrate how to accomplish common geometry processing tasks such as
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computation of differential quantities and operators, real-time deformation,
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parametrization, numerical optimization and remeshing. Each section of the
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lecture notes links to a cross-platform example application.
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# Table of contents
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* [Chapter 1: Introduction to libigl](#chapter1:introductiontolibigl)
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* [Libigl design principles](#libigldesignprinciples)
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* [101 Mesh representation](#meshrepresentation)
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* [102 Visualizing surfaces](#visualizingsurfaces)
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* [103 Interaction with keyboard and mouse](#interactionwithkeyboardandmouse)
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* [104 Scalar field visualization](#scalarfieldvisualization)
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* [105 Overlays](#overlays)
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* [106 Viewer Menu](#viewermenu)
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* [Chapter 2: Discrete Geometric Quantities and
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Operators](#chapter2:discretegeometricquantitiesandoperators)
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* [201 Normals](#normals)
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* [Per-face](#per-face)
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* [Per-vertex](#per-vertex)
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* [Per-corner](#per-corner)
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* [202 Gaussian Curvature](#gaussiancurvature)
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* [203 Curvature Directions](#curvaturedirections)
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* [204 Gradient](#gradient)
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* [204 Laplacian](#laplacian)
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* [Mass matrix](#massmatrix)
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* [Alternative construction of
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Laplacian](#alternativeconstructionoflaplacian)
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* [Chapter 3: Matrices and Linear Algebra](#chapter3:matricesandlinearalgebra)
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* [301 Slice](#slice)
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* [302 Sort](#sort)
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* [Other Matlab-style functions](#othermatlab-stylefunctions)
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* [303 Laplace Equation](#laplaceequation)
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* [Quadratic energy minimization](#quadraticenergyminimization)
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* [304 Linear Equality Constraints](#linearequalityconstraints)
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* [305 Quadratic Programming](#quadraticprogramming)
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* [306 Eigen Decomposition](#eigendecomposition)
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* [Chapter 4: Shape Deformation](#chapter4:shapedeformation)
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* [401 Biharmonic Deformation](#biharmonicdeformation)
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* [402 Polyharmonic Deformation](#polyharmonicdeformation)
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* [403 Bounded Biharmonic Weights](#boundedbiharmonicweights)
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* [404 Dual Quaternion Skinning](#dualquaternionskinning)
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* [405 As-rigid-as-possible](#as-rigid-as-possible)
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* [406 Fast automatic skinning
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transformations](#fastautomaticskinningtransformations)
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* [ARAP with grouped edge-sets](#arapwithgroupededge-sets)
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* [407 Biharmonic Coordinates](#biharmoniccoordinates)
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* [Chapter 5: Parametrization](#chapter5:parametrization)
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* [501 Harmonic parametrization](#harmonicparametrization)
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* [502 Least-Square Conformal Maps](#leastsquareconformalmaps)
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* [503 As-Rigid-As-Possible](#asrigidaspossible)
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* [504 N-Rotationally symmetric tangent fields](#nrotationallysymmetrictangetfields)
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* [505 Global, seamless integer-grid parametrization](#globalseamlessintegergridparametrization)
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* [506 Anisotropic remeshing using frame fields](#anisotropicremeshingusingframefields)
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* [507 N-PolyVector fields](#npolyvectorfields)
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* [508 Conjugate vector fields](#conjugatevectorfields)
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* [509 Planarization](#planarization)
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* [Chapter 6: External libraries](#chapter6:externallibraries)
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* [601 State serialization](#stateserialization)
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* [602 Mixing Matlab code](#mixingmatlabcode)
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* [Saving a Matlab workspace](#savingamatlabworkspace)
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* [Dumping Eigen matrices to copy and paste into
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Matlab](#dumpingeigenmatricestocopyandpasteintomatlab)
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* [603 Calling libigl functions from Matlab](#callinglibiglfunctionsfrommatlab)
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* [604 Triangulation of closed polygons](#triangulationofclosedpolygons)
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* [605 Tetrahedralization of closed surfaces](#tetrahedralizationofclosedsurfaces)
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* [606 Baking ambient occlusion](#bakingambientocclusion)
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* [607 Picking vertices and faces](#pickingverticesandfaces)
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* [608 Locally Injective Maps](#locallyinjectivemaps)
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* [609 Boolean Operations on Meshes](#booleanoperationsonmeshes)
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* [Chapter 7: Miscellaneous](#chapter7:miscellaneous)
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* [701 Mesh Statistics](#meshstatistics)
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* [702 Generalized Winding Number](#generalizedwindingnumber)
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* [703 Mesh Decimation](#meshdecimation)
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* [704 Signed Distances](#signeddistances)
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* [Chapter 8: Outlook for continuing development](#future)
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# Chapter 1 [chapter1:introductiontolibigl]
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We introduce libigl with a series of self-contained examples. The purpose of
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each example is to showcase a feature of libigl while applying to a practical
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problem in geometry processing. In this chapter, we will present the basic
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concepts of libigl and introduce a simple mesh viewer that allows to
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visualize a surface mesh and its attributes. All the tutorial examples are
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cross-platform and can be compiled on MacOSX, Linux and Windows.
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## libigl design principles [libigldesignprinciples]
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Before getting into the examples, we summarize the main design principles in
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libigl:
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1. **No complex data types.** We mostly use matrices and vectors. This greatly
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favors code reusability and forces the function authors to expose all the
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parameters used by the algorithm.
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2. **Minimal dependencies.** We use external libraries only when necessary and
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we wrap them in a small set of functions.
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3. **Header-only.** It is straight forward to use our library since it is only
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one additional include directory in your project. (if you are worried about
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compilation speed, it is also possible to build the library as a [static
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library](../optional/))
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4. **Function encapsulation.** Every function (including its full
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implementation) is contained in a pair of .h/.cpp files with the same name of
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the function.
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### Downloading libigl
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libigl can be downloaded from our [github
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repository](https://github.com/libigl/libigl) or cloned with git:
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```bash
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git clone --recursive https://github.com/libigl/libigl.git
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```
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The core libigl functionality only depends on the C++ Standard Library and
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Eigen.
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To build all the examples in the tutorial, you can use the CMakeLists.txt in
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the tutorial folder:
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```bash
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cd tutorial
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mkdir build
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cd build
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cmake -DCMAKE_BUILD_TYPE=Release ../
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make
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```
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The examples can also be built independently using the CMakeLists.txt
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inside each example folder.
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A few examples in Chapter 5 requires the [CoMiSo
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solver](http://www.graphics.rwth-aachen.de/software/comiso) which has to be
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downloaded and compiled separately.
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## Mesh representation [meshrepresentation]
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libigl uses the [Eigen](http://eigen.tuxfamily.org/) library to encode vector
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and matrices. We suggest that you keep the
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[dense](http://eigen.tuxfamily.org/dox/group__QuickRefPage.html) and
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[sparse](http://eigen.tuxfamily.org/dox/group__SparseQuickRefPage.html) quick
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reference guides at hand while you read the examples in this tutorial.
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A triangular mesh is encoded as a pair of matrices:
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```cpp
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Eigen::MatrixXd V;
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Eigen::MatrixXi F;
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```
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`V` is a #N by 3 matrix which stores the coordinates of the vertices. Each
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row stores the coordinate of a vertex, with its x,y and z coordinates in the first,
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second and third column, respectively. The matrix `F` stores the triangle
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connectivity: each line of `F` denotes a triangle whose 3 vertices are
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represented as indices pointing to rows of `V`.
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Note that the order of the vertex indices in `F` determines the orientation of
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the triangles and it should thus be consistent for the entire surface.
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This simple representation has many advantages:
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1. it is memory efficient and cache friendly
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2. the use of indices instead of pointers greatly simplifies debugging
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3. the data can be trivially copied and serialized
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libigl provides input [output] functions to read [write] many common mesh formats.
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The IO functions are contained in the files read\*.h and write\*.h. As a general
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rule each libigl function is contained in a pair of .h/.cpp files with the same name.
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By default, the .h files include the corresponding cpp files, making the library header-only.
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Reading a mesh from a file requires a single libigl function call:
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```cpp
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igl::readOFF("../shared/cube.off", V, F);
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```
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The function reads the mesh cube.off and it fills the provided `V` and `F` matrices.
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Similarly, a mesh can be written in an OBJ file using:
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```cpp
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igl::writeOBJ("cube.obj",V,F);
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```
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[Example 101](101_FileIO/main.cpp) contains a simple mesh
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converter from OFF to OBJ format.
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## Visualizing surfaces [visualizingsurfaces]
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Libigl provides an glfw-based OpenGL 3.2 viewer to visualize surfaces, their
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properties and additional debugging informations.
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The following code ([Example 102](102_DrawMesh/main.cpp)) is a basic skeleton
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for all the examples that will be used in the tutorial.
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It is a standalone application that loads a mesh and uses the viewer to
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render it.
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```cpp
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#include <igl/readOFF.h>
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#include <igl/viewer/Viewer.h>
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Eigen::MatrixXd V;
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Eigen::MatrixXi F;
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int main(int argc, char *argv[])
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{
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// Load a mesh in OFF format
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igl::readOFF("../shared/bunny.off", V, F);
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// Plot the mesh
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igl::Viewer viewer;
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viewer.data.set_mesh(V, F);
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viewer.launch();
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}
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```
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The function `set_mesh` copies the mesh into the viewer.
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`Viewer.launch()` creates a window, an OpenGL context and it starts the draw loop.
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Additional properties can be plotted on the mesh (as we will see later),
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and it is possible to extend the viewer with standard OpenGL code.
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Please see the documentation in
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[Viewer.h](../include/igl/Viewer/Viewer.h) for more details.
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) loads and draws a
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mesh.](images/102_DrawMesh.png)
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## Interaction with keyboard and mouse [interactionwithkeyboardandmouse]
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Keyboard and mouse events triggers callbacks that can be registered in the
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viewer. The viewer supports the following callbacks:
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```cpp
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bool (*callback_pre_draw)(Viewer& viewer);
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bool (*callback_post_draw)(Viewer& viewer);
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bool (*callback_mouse_down)(Viewer& viewer, int button, int modifier);
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bool (*callback_mouse_up)(Viewer& viewer, int button, int modifier);
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bool (*callback_mouse_move)(Viewer& viewer, int mouse_x, int mouse_y);
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bool (*callback_mouse_scroll)(Viewer& viewer, float delta_y);
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bool (*callback_key_down)(Viewer& viewer, unsigned char key, int modifiers);
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bool (*callback_key_up)(Viewer& viewer, unsigned char key, int modifiers);
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```
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A keyboard callback can be used to visualize multiple meshes or different
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stages of an algorithm, as demonstrated in [Example 103](103_Events/main.cpp), where
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the keyboard callback changes the visualized mesh depending on the key pressed:
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```cpp
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bool key_down(igl::Viewer& viewer, unsigned char key, int modifier)
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{
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if (key == '1')
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{
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viewer.data.clear();
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viewer.data.set_mesh(V1, F1);
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viewer.core.align_camera_center(V1,F1);
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}
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else if (key == '2')
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{
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viewer.data.clear();
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viewer.data.set_mesh(V2, F2);
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viewer.core.align_camera_center(V2,F2);
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}
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return false;
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}
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```
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The callback is registered in the viewer as follows:
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```cpp
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viewer.callback_key_down = &key_down;
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```
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Note that the mesh is cleared before using set_mesh. This has to be called
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every time the number of vertices or faces of the plotted mesh changes. Every
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callback returns a boolean value that tells the viewer if the event has been
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handled by the plugin, or if the viewer should process it normally. This is
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useful, for example, to disable the default mouse event handling if you want to
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control the camera directly in your code.
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The viewer can be extended using plugins, which are classes that implements all
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the viewer's callbacks. See the
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[Viewer_plugin](../include/igl/viewer/ViewerPlugin.h) for more details.
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## Scalar field visualization [scalarfieldvisualization]
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Colors and normals can be associated to faces or vertices using the
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set_colors function:
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```cpp
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viewer.data.set_colors(C);
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```
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`C` is a #C by 3 matrix with one RGB color per row. `C` must have as many
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rows as the number of faces **or** the number of vertices of the mesh.
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Depending on the size of `C`, the viewer applies the color to the faces or to
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the vertices.
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Colors can be used to visualize a scalar function defined on a surface. The
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scalar function is converted to colors using a color transfer function, which
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maps a scalar value between 0 and 1 to a color. A simple example of a scalar
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field defined on a surface is the z coordinate of each point, which can be
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extract from our mesh representation by taking the last column of `V`
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([Example 104](104_Colors/main.cpp)). The function `igl::jet` can be used to
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convert it to colors:
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```cpp
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Eigen::VectorXd Z = V.col(2);
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igl::jet(Z,true,C);
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```
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The first row extracts the third column from `V` (the z coordinate of each
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vertex) and the second calls a libigl functions that converts a scalar field to colors. The second parameter of jet normalizes the scalar field to lie between 0 and 1 before applying the transfer function.
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) igl::jet converts a scalar field to a
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color field.](images/104_Colors.png)
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`igl::jet` is an example of a standard function in libigl: it takes simple
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types and can be easily reused for many different tasks. Not committing to
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heavy data structures types favors simplicity, ease of use and reusability.
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## Overlays [overlays]
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In addition to plotting the surface, the viewer supports the visualization of points, lines and text labels: these overlays can be very helpful while developing geometric processing algorithms to plot debug informations.
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```cpp
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viewer.data.add_points(P,Eigen::RowVector3d(r,g,b));
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```
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Draws a point of color r,g,b for each row of P. The point is placed at the coordinates specified in each row of P, which is a #P by 3 matrix.
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```cpp
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viewer.data.add_edges(P1,P2,Eigen::RowVector3d(r,g,b);
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```
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Draws a line of color r,g,b for each row of P1 and P2, which connects the 3D point in to the point in P2. Both P1 and P2 are of size #P by 3.
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```cpp
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viewer.data.add_label(p,str);
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```
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Draws a label containing the string str at the position p, which is a vector of length 3.
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These functions are demonstrate in [Example 105](105_Overlays/main.cpp) where
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the bounding box of a mesh is plotted using lines and points.
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Using matrices to encode the mesh and its attributes allows to write short and
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efficient code for many operations, avoiding to write for loops. For example,
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the bounding box of a mesh can be found by taking the colwise maximum and minimum of `V`:
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```cpp
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Eigen::Vector3d m = V.colwise().minCoeff();
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Eigen::Vector3d M = V.colwise().maxCoeff();
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```
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) The bounding box of a mesh is shown
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using overlays.](images/105_Overlays.png)
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## Viewer Menu [viewermenu]
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As of version 1.2 the viewer uses a new menu and completely replaces [AntTweakBar](http://anttweakbar.sourceforge.net/doc/). It is based on the open-source projects [nanovg](https://github.com/memononen/nanovg) and [nanogui](https://github.com/wjakob/nanogui). To extend the default menu of the viewer and to expose more user defined variables you have to define a callback function:
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```cpp
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igl::viewer::Viewer viewer;
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bool boolVariable = true;
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float floatVariable = 0.1f;
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// extend viewer menu
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viewer.callback_init = [&](igl::viewer::Viewer& viewer)
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{
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// add new group
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viewer.ngui->addNewGroup("New Group");
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// expose variables
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viewer.ngui->addVariable(boolVariable,"bool");
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viewer.ngui->addVariable(floatVariable,"float");
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// add button
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viewer.ngui->addButton("Print Hello",[](){ cout << "Hello\n"; });
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// call to generate menu
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viewer.ngui->layout();
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return false;
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};
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// start viewer
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viewer.launch();
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```
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If you need a separate new menu window use:
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```cpp
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viewer.ngui->addNewWindow(Eigen::Vector2i(220,10),"New Window");
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```
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You can also switch between different orientation of the layout:
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```cpp
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// horizontal alignment
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viewer.ngui->addNewGroup("New Group",nanogui::FormScreen::Layout::Horizontal);
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viewer.ngui->addButton("Print Test1",[](){ cout << "Test1\n"; });
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viewer.ngui->addButton("Print Test2",[](){ cout << "Test2\n"; });
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// vertical alignment
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viewer.ngui->setCurrentLayout(nanogui::FormScreen::Layout::Vertical);
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viewer.ngui->addVariable(boolVariable,"bool");
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viewer.ngui->addVariable(floatVariable,"float");
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```
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If you do not want to expose variables directly but rather use the get/set functionality:
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```cpp
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viewer.ngui->addVariable([&](bool val) {
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boolVariable = val; // set
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},[&]() {
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return boolVariable; // get
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},"bool",true);
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```
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) The UI of the viewer can be easily customized.](images/106_ViewerMenu.png)
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# Chapter 2: Discrete Geometric Quantities and Operators
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This chapter illustrates a few discrete quantities that libigl can compute on a
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mesh and the libigl functions that construct popular discrete differential
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geometry operators. It also provides an introduction to basic drawing and coloring routines of our viewer.
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## Normals
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Surface normals are a basic quantity necessary for rendering a surface. There
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are a variety of ways to compute and store normals on a triangle mesh. [Example 201](201_Normals/main.cpp) demonstrates how to compute and visualize normals with libigl.
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### Per-face
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Normals are well defined on each triangle of a mesh as the vector orthogonal to
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triangle's plane. These piecewise-constant normals produce piecewise-flat
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renderings: the surface appears non-smooth and reveals its underlying
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discretization.
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### Per-vertex
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Normals can be computed and stored on vertices, and interpolated in the interior of the triangles to produce smooth renderings ([Phong shading](http://en.wikipedia.org/wiki/Phong_shading)).
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Most techniques for computing per-vertex normals take an average of incident face normals. The main difference between these techniques is their weighting scheme: Uniform
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weighting is heavily biased by the discretization choice, whereas area-based
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or angle-based weighting is more forgiving.
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The typical half-edge style computation of area-based weights has this structure:
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```cpp
|
|
N.setZero(V.rows(),3);
|
|
for(int i : vertices)
|
|
{
|
|
for(face : incident_faces(i))
|
|
{
|
|
N.row(i) += face.area * face.normal;
|
|
}
|
|
}
|
|
N.rowwise().normalize();
|
|
```
|
|
|
|
At first glance, it might seem inefficient to loop over incident faces---and thus constructing the per-vertex normals--- without using an half-edge data structure. However, per-vertex normals may be _throwing_ each face normal to
|
|
running sums on its corner vertices:
|
|
|
|
```cpp
|
|
N.setZero(V.rows(),3);
|
|
for(int f = 0; f < F.rows();f++)
|
|
{
|
|
for(int c = 0; c < 3;c++)
|
|
{
|
|
N.row(F(f,c)) += area(f) * face_normal.row(f);
|
|
}
|
|
}
|
|
N.rowwise().normalize();
|
|
```
|
|
|
|
### Per-corner
|
|
|
|
Storing normals per-corner is an efficient and convenient way of supporting both
|
|
smooth and sharp (e.g. creases and corners) rendering. This format is common to
|
|
OpenGL and the .obj mesh file format. Often such normals are tuned by the mesh
|
|
designer, but creases and corners can also be computed automatically. Libigl
|
|
implements a simple scheme which computes corner normals as averages of
|
|
normals of faces incident on the corresponding vertex which do not deviate by more than a specified dihedral angle (e.g. 20°).
|
|
|
|
|
|
|
|
## Gaussian curvature
|
|
Gaussian curvature on a continuous surface is defined as the product of the
|
|
principal curvatures:
|
|
|
|
$k_G = k_1 k_2.$
|
|
|
|
As an _intrinsic_ measure, it depends on the metric and
|
|
not the surface's embedding.
|
|
|
|
Intuitively, Gaussian curvature tells how locally spherical or _elliptic_ the
|
|
surface is ( $k_G>0$ ), how locally saddle-shaped or _hyperbolic_ the surface
|
|
is ( $k_G<0$ ), or how locally cylindrical or _parabolic_ ( $k_G=0$ ) the
|
|
surface is.
|
|
|
|
In the discrete setting, one definition for a "discrete Gaussian curvature"
|
|
on a triangle mesh is via a vertex's _angular deficit_:
|
|
|
|
$k_G(v_i) = 2π - \sum\limits_{j\in N(i)}θ_{ij},$
|
|
|
|
where $N(i)$ are the triangles incident on vertex $i$ and $θ_{ij}$ is the angle
|
|
at vertex $i$ in triangle $j$ [][#meyer_2003].
|
|
|
|
Just like the continuous analog, our discrete Gaussian curvature reveals
|
|
elliptic, hyperbolic and parabolic vertices on the domain, as demonstrated in [Example 202](202GaussianCurvature/main.cpp).
|
|
|
|
|
|
|
|
## Curvature directions
|
|
The two principal curvatures $(k_1,k_2)$ at a point on a surface measure how
|
|
much the surface bends in different directions. The directions of maximum and
|
|
minimum (signed) bending are called principal directions and are always
|
|
orthogonal.
|
|
|
|
Mean curvature is defined as the average of principal curvatures:
|
|
|
|
$H = \frac{1}{2}(k_1 + k_2).$
|
|
|
|
One way to extract mean curvature is by examining the Laplace-Beltrami operator
|
|
applied to the surface positions. The result is a so-called mean-curvature
|
|
normal:
|
|
|
|
$-\Delta \mathbf{x} = H \mathbf{n}.$
|
|
|
|
It is easy to compute this on a discrete triangle mesh in libigl using the
|
|
cotangent Laplace-Beltrami operator [][#meyer_2003].
|
|
|
|
```cpp
|
|
#include <igl/cotmatrix.h>
|
|
#include <igl/massmatrix.h>
|
|
#include <igl/invert_diag.h>
|
|
...
|
|
MatrixXd HN;
|
|
SparseMatrix<double> L,M,Minv;
|
|
igl::cotmatrix(V,F,L);
|
|
igl::massmatrix(V,F,igl::MASSMATRIX_TYPE_VORONOI,M);
|
|
igl::invert_diag(M,Minv);
|
|
HN = -Minv*(L*V);
|
|
H = HN.rowwise().norm(); //up to sign
|
|
```
|
|
|
|
Combined with the angle defect definition of discrete Gaussian curvature, one
|
|
can define principal curvatures and use least squares fitting to find
|
|
directions [][#meyer_2003].
|
|
|
|
Alternatively, a robust method for determining principal curvatures is via
|
|
quadric fitting [][#panozzo_2010]. In the neighborhood around every vertex, a
|
|
best-fit quadric is found and principal curvature values and directions are
|
|
analytically computed on this quadric ([Example
|
|
203](203_curvatureDirections/main.cpp)).
|
|
|
|
|
|
|
|
## Gradient
|
|
Scalar functions on a surface can be discretized as a piecewise linear function
|
|
with values defined at each mesh vertex:
|
|
|
|
$f(\mathbf{x}) \approx \sum\limits_{i=1}^n \phi_i(\mathbf{x})\, f_i,$
|
|
|
|
where $\phi_i$ is a piecewise linear hat function defined by the mesh so that
|
|
for each triangle $\phi_i$ is _the_ linear function which is one only at
|
|
vertex $i$ and zero at the other corners.
|
|
|
|
|
|
|
|
Thus gradients of such piecewise linear functions are simply sums of gradients
|
|
of the hat functions:
|
|
|
|
$\nabla f(\mathbf{x}) \approx
|
|
\nabla \sum\limits_{i=1}^n \phi_i(\mathbf{x})\, f_i =
|
|
\sum\limits_{i=1}^n \nabla \phi_i(\mathbf{x})\, f_i.$
|
|
|
|
This reveals that the gradient is a linear function of the vector of $f_i$
|
|
values. Because the $\phi_i$ are linear in each triangle, their gradients are
|
|
_constant_ in each triangle. Thus our discrete gradient operator can be written
|
|
as a matrix multiplication taking vertex values to triangle values:
|
|
|
|
$\nabla f \approx \mathbf{G}\,\mathbf{f},$
|
|
|
|
where $\mathbf{f}$ is $n\times 1$ and $\mathbf{G}$ is an $md\times n$ sparse
|
|
matrix. This matrix $\mathbf{G}$ can be derived geometrically, e.g.
|
|
[ch. 2][#jacobson_thesis_2013].
|
|
Libigl's `grad` function computes $\mathbf{G}$ for
|
|
triangle and tetrahedral meshes ([Example 204](204_Gradient/main.cpp)):
|
|
|
|
|
|
|
|
## Laplacian
|
|
|
|
The discrete Laplacian is an essential geometry processing tool. Many
|
|
interpretations and flavors of the Laplace and Laplace-Beltrami operator exist.
|
|
|
|
In open Euclidean space, the _Laplace_ operator is the usual divergence of
|
|
gradient (or equivalently the Laplacian of a function is the trace of its
|
|
Hessian):
|
|
|
|
$\Delta f =
|
|
\frac{\partial^2 f}{\partial x^2} +
|
|
\frac{\partial^2 f}{\partial y^2} +
|
|
\frac{\partial^2 f}{\partial z^2}.$
|
|
|
|
The _Laplace-Beltrami_ operator generalizes this to surfaces.
|
|
|
|
When considering piecewise-linear functions on a triangle mesh, a discrete
|
|
Laplacian may be derived in a variety of ways. The most popular in geometry
|
|
processing is the so-called ``cotangent Laplacian'' $\mathbf{L}$, arising
|
|
simultaneously from FEM, DEC and applying divergence theorem to vertex
|
|
one-rings. As a linear operator taking vertex values to vertex values, the
|
|
Laplacian $\mathbf{L}$ is a $n\times n$ matrix with elements:
|
|
|
|
$L_{ij} = \begin{cases}j \in N(i) &\cot \alpha_{ij} + \cot \beta_{ij},\\
|
|
j \notin N(i) & 0,\\
|
|
i = j & -\sum\limits_{k\neq i} L_{ik},
|
|
\end{cases}$
|
|
|
|
where $N(i)$ are the vertices adjacent to (neighboring) vertex $i$, and
|
|
$\alpha_{ij},\beta_{ij}$ are the angles opposite to edge ${ij}$.
|
|
This formula leads to a typical half-edge style implementation for
|
|
constructing $\mathbf{L}$:
|
|
|
|
```cpp
|
|
for(int i : vertices)
|
|
{
|
|
for(int j : one_ring(i))
|
|
{
|
|
for(int k : triangle_on_edge(i,j))
|
|
{
|
|
L(i,j) = cot(angle(i,j,k));
|
|
L(i,i) -= cot(angle(i,j,k));
|
|
}
|
|
}
|
|
}
|
|
```
|
|
|
|
Similarly as before, it may seem to loop over one-rings without having an half-edge data structure. However, this is not the case, since the Laplacian may be built by summing together contributions for each triangle, much in spirit with its FEM discretization
|
|
of the Dirichlet energy (sum of squared gradients):
|
|
|
|
```cpp
|
|
for(triangle t : triangles)
|
|
{
|
|
for(edge i,j : t)
|
|
{
|
|
L(i,j) += cot(angle(i,j,k));
|
|
L(j,i) += cot(angle(i,j,k));
|
|
L(i,i) -= cot(angle(i,j,k));
|
|
L(j,j) -= cot(angle(i,j,k));
|
|
}
|
|
}
|
|
```
|
|
|
|
Libigl implements discrete "cotangent" Laplacians for triangles meshes and
|
|
tetrahedral meshes, building both with fast geometric rules rather than "by the
|
|
book" FEM construction which involves many (small) matrix inversions, cf.
|
|
[#sharf_2007][].
|
|
|
|
The operator applied to mesh vertex positions amounts to smoothing by _flowing_
|
|
the surface along the mean curvature normal direction ([Example 205](205_Laplacian/main.cpp)). Note that this is equivalent to minimizing surface area.
|
|
|
|
![The `Laplacian` example computes conformalized mean curvature flow using the
|
|
cotangent Laplacian [#kazhdan_2012][].](images/cow-curvature-flow.jpg)
|
|
|
|
### Mass matrix
|
|
The mass matrix $\mathbf{M}$ is another $n \times n$ matrix which takes vertex
|
|
values to vertex values. From an FEM point of view, it is a discretization of
|
|
the inner-product: it accounts for the area around each vertex. Consequently,
|
|
$\mathbf{M}$ is often a diagonal matrix, such that $M_{ii}$ is the barycentric
|
|
or voronoi area around vertex $i$ in the mesh [#meyer_2003][]. The inverse of
|
|
this matrix is also very useful as it transforms integrated quantities into
|
|
point-wise quantities, e.g.:
|
|
|
|
$\Delta f \approx \mathbf{M}^{-1} \mathbf{L} \mathbf{f}.$
|
|
|
|
In general, when encountering squared quantities integrated over the surface,
|
|
the mass matrix will be used as the discretization of the inner product when
|
|
sampling function values at vertices:
|
|
|
|
$\int_S x\, y\ dA \approx \mathbf{x}^T\mathbf{M}\,\mathbf{y}.$
|
|
|
|
An alternative mass matrix $\mathbf{T}$ is a $md \times md$ matrix which takes
|
|
triangle vector values to triangle vector values. This matrix represents an
|
|
inner-product accounting for the area associated with each triangle (i.e. the
|
|
triangles true area).
|
|
|
|
### Alternative construction of Laplacian
|
|
|
|
An alternative construction of the discrete cotangent Laplacian is by
|
|
"squaring" the discrete gradient operator. This may be derived by applying
|
|
Green's identity (ignoring boundary conditions for the moment):
|
|
|
|
$\int_S \|\nabla f\|^2 dA = \int_S f \Delta f dA$
|
|
|
|
Or in matrix form which is immediately translatable to code:
|
|
|
|
$\mathbf{f}^T \mathbf{G}^T \mathbf{T} \mathbf{G} \mathbf{f} =
|
|
\mathbf{f}^T \mathbf{M} \mathbf{M}^{-1} \mathbf{L} \mathbf{f} =
|
|
\mathbf{f}^T \mathbf{L} \mathbf{f}.$
|
|
|
|
So we have that $\mathbf{L} = \mathbf{G}^T \mathbf{T} \mathbf{G}$. This also
|
|
hints that we may consider $\mathbf{G}^T$ as a discrete _divergence_ operator,
|
|
since the Laplacian is the divergence of the gradient. Naturally, $\mathbf{G}^T$ is
|
|
a $n \times md$ sparse matrix which takes vector values stored at triangle faces
|
|
to scalar divergence values at vertices.
|
|
|
|
# Chapter 3: Matrices and linear algebra
|
|
Libigl relies heavily on the Eigen library for dense and sparse linear algebra
|
|
routines. Besides geometry processing routines, libigl has linear algebra
|
|
routines which bootstrap Eigen and make it feel even more similar to a high-level
|
|
algebra library such as Matlab.
|
|
|
|
## Slice
|
|
A very familiar and powerful routine in Matlab is array slicing. This allows
|
|
reading from or writing to a possibly non-contiguous sub-matrix. Let's consider
|
|
the Matlab code:
|
|
|
|
```matlab
|
|
B = A(R,C);
|
|
```
|
|
|
|
If `A` is a $m \times n$ matrix and `R` is a $j$-long list of row-indices
|
|
(between 1 and $m$) and `C` is a $k$-long list of column-indices, then as a
|
|
result `B` will be a $j \times k$ matrix drawing elements from `A` according to
|
|
`R` and `C`. In libigl, the same functionality is provided by the `slice`
|
|
function ([Example 301](301_Slice/main.cpp)):
|
|
|
|
```cpp
|
|
VectorXi R,C;
|
|
MatrixXd A,B;
|
|
...
|
|
igl::slice(A,R,C,B);
|
|
```
|
|
|
|
Note that `A` and `B` could also be sparse matrices.
|
|
|
|
Similarly, consider the Matlab code:
|
|
|
|
```matlab
|
|
A(R,C) = B;
|
|
```
|
|
|
|
Now, the selection is on the left-hand side so the $j \times k$ matrix `B` is
|
|
being _written into_ the submatrix of `A` determined by `R` and `C`. This
|
|
functionality is provided in libigl using `slice_into`:
|
|
|
|
```cpp
|
|
igl::slice_into(B,R,C,A);
|
|
```
|
|
|
|
|
|
|
|
## Sort
|
|
|
|
Matlab and other higher-level languages make it very easy to extract indices of
|
|
sorting and comparison routines. For example in Matlab, one can write:
|
|
|
|
```matlab
|
|
[Y,I] = sort(X,1,'ascend');
|
|
```
|
|
|
|
so if `X` is a $m \times n$ matrix then `Y` will also be an $m \times n$ matrix
|
|
with entries sorted along dimension `1` in `'ascend'`ing order. The second
|
|
output `I` is a $m \times n$ matrix of indices such that `Y(i,j) =
|
|
X(I(i,j),j);`. That is, `I` reveals how `X` is sorted into `Y`.
|
|
|
|
This same functionality is supported in libigl:
|
|
|
|
```cpp
|
|
igl::sort(X,1,true,Y,I);
|
|
```
|
|
|
|
Similarly, sorting entire rows can be accomplished in Matlab using:
|
|
|
|
```matlab
|
|
[Y,I] = sortrows(X,'ascend');
|
|
```
|
|
|
|
where now `I` is a $m$ vector of indices such that `Y = X(I,:)`.
|
|
|
|
In libigl, this is supported with
|
|
|
|
```cpp
|
|
igl::sortrows(X,true,Y,I);
|
|
```
|
|
where again `I` reveals the index of sort so that it can be reproduced with
|
|
`igl::slice(X,I,1,Y)`.
|
|
|
|
Analogous functions are available in libigl for: `max`, `min`, and `unique`.
|
|
|
|
).](images/decimated-knight-sort-color.jpg)
|
|
|
|
|
|
### Other Matlab-style functions
|
|
Libigl implements a variety of other routines with the same api and
|
|
functionality as common Matlab functions.
|
|
|
|
| Name | Description |
|
|
| :----------------------- | :---------------------------------------------------------------------------------- |
|
|
| `igl::any_of` | Whether any elements are non-zero (true) |
|
|
| `igl::cat` | Concatenate two matrices (especially useful for dealing with Eigen sparse matrices) |
|
|
| `igl::ceil` | Round entries up to nearest integer |
|
|
| `igl::cumsum` | Cumulative sum of matrix elements |
|
|
| `igl::colon` | Act like Matlab's `:`, similar to Eigen's `LinSpaced` |
|
|
| `igl::cross` | Cross product per-row |
|
|
| `igl::dot` | dot product per-row |
|
|
| `igl::find` | Find subscripts of non-zero entries |
|
|
| `igl::floor` | Round entries down to nearest integer |
|
|
| `igl::histc` | Counting occurrences for building a histogram |
|
|
| `igl::hsv_to_rgb` | Convert HSV colors to RGB (cf. Matlab's `hsv2rgb`) |
|
|
| `igl::intersect` | Set intersection of matrix elements. |
|
|
| `igl::jet` | Quantized colors along the rainbow. |
|
|
| `igl::kronecker_product` | Compare to Matlab's `kronprod` |
|
|
| `igl::median` | Compute the median per column |
|
|
| `igl::mode` | Compute the mode per column |
|
|
| `igl::null` | Compute the null space basis of a matrix |
|
|
| `igl::nchoosek` | Compute all k-size combinations of n-long vector |
|
|
| `igl::orth` | Orthogonalization of a basis |
|
|
| `igl::parula` | Generate a quantized colormap from blue to yellow |
|
|
| `igl::randperm` | Generate a random permutation of [0,...,n-1] |
|
|
| `igl::rgb_to_hsv` | Convert RGB colors to HSV (cf. Matlab's `rgb2hsv`) |
|
|
| `igl::setdiff` | Set difference of matrix elements |
|
|
| `igl::sort` | Sort elements or rows of matrix |
|
|
| `igl::speye` | Identity as sparse matrix |
|
|
| `igl::sum` | Sum along columns or rows (of sparse matrix) |
|
|
| `igl::unique` | Extract unique elements or rows of matrix |
|
|
|
|
## Laplace equation
|
|
A common linear system in geometry processing is the Laplace equation:
|
|
|
|
$∆z = 0$
|
|
|
|
subject to some boundary conditions, for example Dirichlet boundary conditions
|
|
(fixed value):
|
|
|
|
$\left.z\right|_{\partial{S}} = z_{bc}$
|
|
|
|
In the discrete setting, the linear system can be written as:
|
|
|
|
$\mathbf{L} \mathbf{z} = \mathbf{0}$
|
|
|
|
where $\mathbf{L}$ is the $n \times n$ discrete Laplacian and $\mathbf{z}$ is a
|
|
vector of per-vertex values. Most of $\mathbf{z}$ correspond to interior
|
|
vertices and are unknown, but some of $\mathbf{z}$ represent values at boundary
|
|
vertices. Their values are known so we may move their corresponding terms to
|
|
the right-hand side.
|
|
|
|
Conceptually, this is very easy if we have sorted $\mathbf{z}$ so that interior
|
|
vertices come first and then boundary vertices:
|
|
|
|
$$\left(\begin{array}{cc}
|
|
\mathbf{L}_{in,in} & \mathbf{L}_{in,b}\\
|
|
\mathbf{L}_{b,in} & \mathbf{L}_{b,b}\end{array}\right)
|
|
\left(\begin{array}{c}
|
|
\mathbf{z}_{in}\\
|
|
\mathbf{z}_{b}\end{array}\right) =
|
|
\left(\begin{array}{c}
|
|
\mathbf{0}_{in}\\
|
|
\mathbf{z}_{bc}\end{array}\right)$$
|
|
|
|
The bottom block of equations is no longer meaningful so we'll only consider
|
|
the top block:
|
|
|
|
$$\left(\begin{array}{cc}
|
|
\mathbf{L}_{in,in} & \mathbf{L}_{in,b}\end{array}\right)
|
|
\left(\begin{array}{c}
|
|
\mathbf{z}_{in}\\
|
|
\mathbf{z}_{b}\end{array}\right) =
|
|
\mathbf{0}_{in}$$
|
|
|
|
We can move the known values to the right-hand side:
|
|
|
|
$$\mathbf{L}_{in,in}
|
|
\mathbf{z}_{in} = -
|
|
\mathbf{L}_{in,b}
|
|
\mathbf{z}_{b}$$
|
|
|
|
Finally we can solve this equation for the unknown values at interior vertices
|
|
$\mathbf{z}_{in}$.
|
|
|
|
However, our vertices will often not be sorted in this way. One option would be to sort `V`,
|
|
then proceed as above and then _unsort_ the solution `Z` to match `V`. However,
|
|
this solution is not very general.
|
|
|
|
With array slicing no explicit sort is needed. Instead we can _slice-out_
|
|
submatrix blocks ($\mathbf{L}_{in,in}$, $\mathbf{L}_{in,b}$, etc.) and follow
|
|
the linear algebra above directly. Then we can slice the solution _into_ the
|
|
rows of `Z` corresponding to the interior vertices ([Example 303](303_LaplaceEquation/main.cpp)).
|
|
|
|
|
|
|
|
### Quadratic energy minimization
|
|
|
|
The same Laplace equation may be equivalently derived by minimizing Dirichlet
|
|
energy subject to the same boundary conditions:
|
|
|
|
$\mathop{\text{minimize }}_z \frac{1}{2}\int\limits_S \|\nabla z\|^2 dA$
|
|
|
|
On our discrete mesh, recall that this becomes
|
|
|
|
$\mathop{\text{minimize }}_\mathbf{z} \frac{1}{2}\mathbf{z}^T \mathbf{G}^T \mathbf{D}
|
|
\mathbf{G} \mathbf{z} \rightarrow \mathop{\text{minimize }}_\mathbf{z} \mathbf{z}^T \mathbf{L} \mathbf{z}$
|
|
|
|
The general problem of minimizing some energy over a mesh subject to fixed
|
|
value boundary conditions is so wide spread that libigl has a dedicated api for
|
|
solving such systems.
|
|
|
|
Let us consider a general quadratic minimization problem subject to different
|
|
common constraints:
|
|
|
|
$$\mathop{\text{minimize }}_\mathbf{z} \frac{1}{2}\mathbf{z}^T \mathbf{Q} \mathbf{z} +
|
|
\mathbf{z}^T \mathbf{B} + \text{constant},$$
|
|
|
|
subject to
|
|
|
|
$$\mathbf{z}_b = \mathbf{z}_{bc} \text{ and } \mathbf{A}_{eq} \mathbf{z} =
|
|
\mathbf{B}_{eq},$$
|
|
|
|
where
|
|
|
|
- $\mathbf{Q}$ is a (usually sparse) $n \times n$ positive semi-definite
|
|
matrix of quadratic coefficients (Hessian),
|
|
- $\mathbf{B}$ is a $n \times 1$ vector of linear coefficients,
|
|
- $\mathbf{z}_b$ is a $|b| \times 1$ portion of
|
|
$\mathbf{z}$ corresponding to boundary or _fixed_ vertices,
|
|
- $\mathbf{z}_{bc}$ is a $|b| \times 1$ vector of known values corresponding to
|
|
$\mathbf{z}_b$,
|
|
- $\mathbf{A}_{eq}$ is a (usually sparse) $m \times n$ matrix of linear
|
|
equality constraint coefficients (one row per constraint), and
|
|
- $\mathbf{B}_{eq}$ is a $m \times 1$ vector of linear equality constraint
|
|
right-hand side values.
|
|
|
|
This specification is overly general as we could write $\mathbf{z}_b =
|
|
\mathbf{z}_{bc}$ as rows of $\mathbf{A}_{eq} \mathbf{z} =
|
|
\mathbf{B}_{eq}$, but these fixed value constraints appear so often that they
|
|
merit a dedicated place in the API.
|
|
|
|
In libigl, solving such quadratic optimization problems is split into two
|
|
routines: precomputation and solve. Precomputation only depends on the
|
|
quadratic coefficients, known value indices and linear constraint coefficients:
|
|
|
|
```cpp
|
|
igl::min_quad_with_fixed_data mqwf;
|
|
igl::min_quad_with_fixed_precompute(Q,b,Aeq,true,mqwf);
|
|
```
|
|
|
|
The output is a struct `mqwf` which contains the system matrix factorization
|
|
and is used during solving with arbitrary linear terms, known values, and
|
|
constraint in the right-hand sides:
|
|
|
|
```cpp
|
|
igl::min_quad_with_fixed_solve(mqwf,B,bc,Beq,Z);
|
|
```
|
|
|
|
The output `Z` is a $n \times 1$ vector of solutions with fixed values
|
|
correctly placed to match the mesh vertices `V`.
|
|
|
|
## Linear equality constraints
|
|
We saw above that `min_quad_with_fixed_*` in libigl provides a compact way to
|
|
solve general quadratic programs. Let's consider another example, this time
|
|
with active linear equality constraints. Specifically let's solve the
|
|
`bi-Laplace equation` or equivalently minimize the Laplace energy:
|
|
|
|
$$\Delta^2 z = 0 \leftrightarrow \mathop{\text{minimize }}\limits_z \frac{1}{2}
|
|
\int\limits_S (\Delta z)^2 dA$$
|
|
|
|
subject to fixed value constraints and a linear equality constraint:
|
|
|
|
$z_{a} = 1, z_{b} = -1$ and $z_{c} = z_{d}$.
|
|
|
|
Notice that we can rewrite the last constraint in the familiar form from above:
|
|
|
|
$z_{c} - z_{d} = 0.$
|
|
|
|
Now we can assembly `Aeq` as a $1 \times n$ sparse matrix with a coefficient
|
|
$1$ in the column corresponding to vertex $c$ and a $-1$ at $d$. The right-hand
|
|
side `Beq` is simply zero.
|
|
|
|
Internally, `min_quad_with_fixed_*` solves using the Lagrange Multiplier
|
|
method. This method adds additional variables for each linear constraint (in
|
|
general a $m \times 1$ vector of variables $\lambda$) and then solves the
|
|
saddle problem:
|
|
|
|
$$\mathop{\text{find saddle }}_{\mathbf{z},\lambda}\, \frac{1}{2}\mathbf{z}^T \mathbf{Q} \mathbf{z} +
|
|
\mathbf{z}^T \mathbf{B} + \text{constant} + \lambda^T\left(\mathbf{A}_{eq}
|
|
\mathbf{z} - \mathbf{B}_{eq}\right)$$
|
|
|
|
This can be rewritten in a more familiar form by stacking $\mathbf{z}$ and
|
|
$\lambda$ into one $(m+n) \times 1$ vector of unknowns:
|
|
|
|
$$\mathop{\text{find saddle }}_{\mathbf{z},\lambda}\,
|
|
\frac{1}{2}
|
|
\left(
|
|
\mathbf{z}^T
|
|
\lambda^T
|
|
\right)
|
|
\left(
|
|
\begin{array}{cc}
|
|
\mathbf{Q} & \mathbf{A}_{eq}^T\\
|
|
\mathbf{A}_{eq} & 0
|
|
\end{array}
|
|
\right)
|
|
\left(
|
|
\begin{array}{c}
|
|
\mathbf{z}\\
|
|
\lambda
|
|
\end{array}
|
|
\right) +
|
|
\left(
|
|
\mathbf{z}^T
|
|
\lambda^T
|
|
\right)
|
|
\left(
|
|
\begin{array}{c}
|
|
\mathbf{B}\\
|
|
-\mathbf{B}_{eq}
|
|
\end{array}
|
|
\right)
|
|
+ \text{constant}$$
|
|
|
|
Differentiating with respect to $\left( \mathbf{z}^T \lambda^T \right)$ reveals
|
|
a linear system and we can solve for $\mathbf{z}$ and $\lambda$. The only
|
|
difference from the straight quadratic _minimization_ system, is that this
|
|
saddle problem system will not be positive definite. Thus, we must use a
|
|
different factorization technique (LDLT rather than LLT): libigl's
|
|
`min_quad_with_fixed_precompute` automatically chooses the correct solver in
|
|
the presence of linear equality constraints ([Example 304](304_LinearEqualityConstraints/main.cpp)).
|
|
|
|
|
|
|
|
## Quadratic programming
|
|
|
|
We can generalize the quadratic optimization in the previous section even more
|
|
by allowing inequality constraints. Specifically box constraints (lower and
|
|
upper bounds):
|
|
|
|
$\mathbf{l} \le \mathbf{z} \le \mathbf{u},$
|
|
|
|
where $\mathbf{l},\mathbf{u}$ are $n \times 1$ vectors of lower and upper
|
|
bounds
|
|
and general linear inequality constraints:
|
|
|
|
$\mathbf{A}_{ieq} \mathbf{z} \le \mathbf{B}_{ieq},$
|
|
|
|
where $\mathbf{A}_{ieq}$ is a $k \times n$ matrix of linear coefficients and
|
|
$\mathbf{B}_{ieq}$ is a $k \times 1$ matrix of constraint right-hand sides.
|
|
|
|
Again, we are overly general as the box constraints could be written as
|
|
rows of the linear inequality constraints, but bounds appear frequently enough
|
|
to merit a dedicated api.
|
|
|
|
Libigl implements its own active set routine for solving _quadratric programs_
|
|
(QPs). This algorithm works by iteratively "activating" violated inequality
|
|
constraints by enforcing them as equalities and "deactivating" constraints
|
|
which are no longer needed.
|
|
|
|
After deciding which constraints are active at each iteration, the problem
|
|
reduces to a quadratic minimization subject to linear _equality_ constraints,
|
|
and the method from the previous section is invoked. This is repeated until convergence.
|
|
|
|
Currently the implementation is efficient for box constraints and sparse
|
|
non-overlapping linear inequality constraints.
|
|
|
|
Unlike alternative interior-point methods, the active set method benefits from
|
|
a warm-start (initial guess for the solution vector $\mathbf{z}$).
|
|
|
|
```cpp
|
|
igl::active_set_params as;
|
|
// Z is optional initial guess and output
|
|
igl::active_set(Q,B,b,bc,Aeq,Beq,Aieq,Bieq,lx,ux,as,Z);
|
|
```
|
|
|
|
 uses an active set solver to optimize
|
|
discrete biharmonic kernels [#rustamov_2011][] at multiple scales
|
|
.](images/cheburashka-multiscale-biharmonic-kernels.jpg)
|
|
|
|
## Eigen Decomposition
|
|
|
|
Libigl has rudimentary support for extracting eigen pairs of a generalized
|
|
eigen value problem:
|
|
|
|
$Ax = \lambda B x$
|
|
|
|
where $A$ is a sparse symmetric matrix and $B$ is a sparse positive definite
|
|
matrix. Most commonly in geometry processing, we let $A=L$ the cotangent
|
|
Laplacian and $B=M$ the per-vertex mass matrix (e.g. [#vallet_2008][]).
|
|
Typically applications will make use of the _low frequency_ eigen modes.
|
|
Analagous to the Fourier decomposition, a function $f$ on a surface can be
|
|
represented via its spectral decomposition of the eigen modes of the
|
|
Laplace-Beltrami:
|
|
|
|
$f = \sum\limits_{i=1}^\infty a_i \phi_i$
|
|
|
|
where each $\phi_i$ is an eigen function satisfying: $\Delta \phi_i = \lambda_i
|
|
\phi_i$ and $a_i$ are scalar coefficients. For a discrete triangle mesh, a
|
|
completely analogous decomposition exists, albeit with finite sum:
|
|
|
|
$\mathbf{f} = \sum\limits_{i=1}^n a_i \phi_i$
|
|
|
|
where now a column vector of values at vertices $\mathbf{f} \in \mathcal{R}^n$
|
|
specifies a piecewise linear function and $\phi_i \in \mathcal{R}^n$ is an
|
|
eigen vector satisfying:
|
|
|
|
$\mathbf{L} \phi_i = \lambda_i \mathbf{M} \phi_i$.
|
|
|
|
Note that Vallet & Levy [#vallet_2008][] propose solving a symmetrized
|
|
_standard_ eigen problem $\mathbf{M}^{-1/2}\mathbf{L}\mathbf{M}^{-1/2} \phi_i
|
|
= \lambda_i \phi_i$. Libigl implements a generalized eigen problem solver so
|
|
this unnecessary symmetrization can be avoided.
|
|
|
|
Often the sum above is _truncated_ to the first $k$ eigen vectors. If the low
|
|
frequency modes are chosen, i.e. those corresponding to small $\lambda_i$
|
|
values, then this truncation effectively _regularizes_ $\mathbf{f}$ to smooth,
|
|
slowly changing functions over the mesh (e.g. [#hildebrandt_2011][]). Modal
|
|
analysis and model subspaces have been used frequently in real-time deformation
|
|
(e.g. [#barbic_2005][]).
|
|
|
|
In [Example 306](306_EigenDecomposition/main.cpp)), the first 5 eigen vectors
|
|
of the discrete Laplace-Beltrami operator are computed and displayed in
|
|
pseudo-color atop the beetle. Eigen vectors are computed using `igl::eigs`
|
|
(mirroring MATLAB's `eigs`). The 5 eigen vectors are placed into the columns
|
|
of `U` and the eigen values are placed into the entries of `S`:
|
|
|
|
```cpp
|
|
SparseMatrix<double> L,M;
|
|
igl::cotmatrix(V,F,L);
|
|
igl::massmatrix(V,F,igl::MASSMATRIX_TYPE_DEFAULT,M);
|
|
Eigen::MatrixXd U;
|
|
Eigen::VectorXd S;
|
|
igl::eigs(L,M,5,igl::EIGS_TYPE_SM,U,S);
|
|
```
|
|
|
|
) Low frequency eigen vectors
|
|
of the discrete Laplace-Beltrami operator vary smoothly and slowly over the
|
|
_Beetle_.](images/beetle-eigen-decomposition.gif)
|
|
|
|
# Chapter 4: Shape deformation
|
|
Modern mesh-based shape deformation methods satisfy user deformation
|
|
constraints at handles (selected vertices or regions on the mesh) and propagate
|
|
these handle deformations to the rest of shape _smoothly_ and _without removing
|
|
or distorting details_. Libigl provides implementations of a variety of
|
|
state-of-the-art deformation techniques, ranging from quadratic mesh-based
|
|
energy minimizers, to skinning methods, to non-linear elasticity-inspired
|
|
techniques.
|
|
|
|
## Biharmonic deformation
|
|
The period of research between 2000 and 2010 produced a collection of
|
|
techniques that cast the problem of handle-based shape deformation as a
|
|
quadratic energy minimization problem or equivalently the solution to a linear
|
|
partial differential equation.
|
|
|
|
There are many flavors of these techniques, but a prototypical subset are those
|
|
that consider solutions to the bi-Laplace equation, that is a biharmonic
|
|
function [#botsch_2004][]. This fourth-order PDE provides sufficient
|
|
flexibility in boundary conditions to ensure $C^1$ continuity at handle
|
|
constraints (in the limit under refinement) [#jacobson_mixed_2010][].
|
|
|
|
### Biharmonic surfaces
|
|
Let us first begin our discussion of biharmonic _deformation_, by considering
|
|
biharmonic _surfaces_. We will casually define biharmonic surfaces as surface
|
|
whose _position functions_ are biharmonic with respect to some initial
|
|
parameterization:
|
|
|
|
$\Delta^2 \mathbf{x}' = 0$
|
|
|
|
and subject to some handle constraints, conceptualized as "boundary
|
|
conditions":
|
|
|
|
$\mathbf{x}'_{b} = \mathbf{x}_{bc}.$
|
|
|
|
where $\mathbf{x}'$ is the unknown 3D position of a point on the surface. So we
|
|
are asking that the bi-Laplacian of each of spatial coordinate function to be
|
|
zero.
|
|
|
|
In libigl, one can solve a biharmonic problem with `igl::harmonic`
|
|
and setting $k=2$ (_bi_-harmonic):
|
|
|
|
```cpp
|
|
// U_bc contains deformation of boundary vertices b
|
|
igl::harmonic(V,F,b,U_bc,2,U);
|
|
```
|
|
|
|
This produces a smooth surface that interpolates the handle constraints, but all
|
|
original details on the surface will be _smoothed away_. Most obviously, if the
|
|
original surface is not already biharmonic, then giving all handles the
|
|
identity deformation (keeping them at their rest positions) will **not**
|
|
reproduce the original surface. Rather, the result will be the biharmonic
|
|
surface that does interpolate those handle positions.
|
|
|
|
Thus, we may conclude that this is not an intuitive technique for shape
|
|
deformation.
|
|
|
|
### Biharmonic deformation fields
|
|
Now we know that one useful property for a deformation technique is "rest pose
|
|
reproduction": applying no deformation to the handles should apply no
|
|
deformation to the shape.
|
|
|
|
To guarantee this by construction we can work with _deformation fields_ (ie.
|
|
displacements)
|
|
$\mathbf{d}$ rather
|
|
than directly with positions $\mathbf{x}$. Then the deformed positions can be
|
|
recovered as
|
|
|
|
$\mathbf{x}' = \mathbf{x}+\mathbf{d}.$
|
|
|
|
A smooth deformation field $\mathbf{d}$ which interpolates the deformation
|
|
fields of the handle constraints will impose a smooth deformed shape
|
|
$\mathbf{x}'$. Naturally, we consider _biharmonic deformation fields_:
|
|
|
|
$\Delta^2 \mathbf{d} = 0$
|
|
|
|
subject to the same handle constraints, but rewritten in terms of their implied
|
|
deformation field at the boundary (handles):
|
|
|
|
$\mathbf{d}_b = \mathbf{x}_{bc} - \mathbf{x}_b.$
|
|
|
|
Again we can use `igl::harmonic` with $k=2$, but this time solve for the
|
|
deformation field and then recover the deformed positions:
|
|
|
|
```cpp
|
|
// U_bc contains deformation of boundary vertices b
|
|
D_bc = U_bc - igl::slice(V,b,1);
|
|
igl::harmonic(V,F,b,D_bc,2,D);
|
|
U = V+D;
|
|
```
|
|
|
|
 example deforms a statue's head as a _biharmonic
|
|
surface_ (top) and using a _biharmonic displacements_
|
|
(bottom).](images/max-biharmonic.jpg)
|
|
|
|
#### Relationship to "differential coordinates" and Laplacian surface editing
|
|
Biharmonic functions (whether positions or displacements) are solutions to the
|
|
bi-Laplace equation, but also minimizers of the "Laplacian energy". For
|
|
example, for displacements $\mathbf{d}$, the energy reads
|
|
|
|
$\int\limits_S \|\Delta \mathbf{d}\|^2 dA,$
|
|
|
|
where we define $\Delta \mathbf{d}$ to simply apply the Laplacian
|
|
coordinate-wise.
|
|
|
|
By linearity of the Laplace(-Beltrami) operator we can reexpress this energy in
|
|
terms of the original positions $\mathbf{x}$ and the unknown positions
|
|
$\mathbf{x}' = \mathbf{x} - \mathbf{d}$:
|
|
|
|
$\int\limits_S \|\Delta (\mathbf{x}' - \mathbf{x})\|^2 dA = \int\limits_S
|
|
\|\Delta \mathbf{x}' - \Delta \mathbf{x})\|^2 dA.$
|
|
|
|
In the early work of Sorkine et al., the quantities $\Delta \mathbf{x}'$ and
|
|
$\Delta \mathbf{x}$ were dubbed "differential coordinates" [#sorkine_2004][].
|
|
Their deformations (without linearized rotations) is thus equivalent to
|
|
biharmonic deformation fields.
|
|
|
|
## Polyharmonic deformation
|
|
We can generalize biharmonic deformation by considering different powers of
|
|
the Laplacian, resulting in a series of PDEs of the form:
|
|
|
|
$\Delta^k \mathbf{d} = 0.$
|
|
|
|
with $k\in{1,2,3,\dots}$. The choice of $k$ determines the level of continuity
|
|
at the handles. In particular, $k=1$ implies $C^0$ at the boundary, $k=2$
|
|
implies $C^1$, $k=3$ implies $C^2$ and in general $k$ implies $C^{k-1}$.
|
|
|
|
```cpp
|
|
int k = 2;// or 1,3,4,...
|
|
igl::harmonic(V,F,b,bc,k,Z);
|
|
```
|
|
|
|
 example deforms a flat domain (left) into a bump as a
|
|
solution to various $k$-harmonic PDEs.](images/bump-k-harmonic.jpg)
|
|
|
|
## Bounded biharmonic weights
|
|
In computer animation, shape deformation is often referred to as "skinning".
|
|
Constraints are posed as relative rotations of internal rigid "bones" inside a
|
|
character. The deformation method, or skinning method, determines how the
|
|
surface of the character (i.e. its skin) should move as a function of the bone
|
|
rotations.
|
|
|
|
The most popular technique is linear blend skinning. Each point on the shape
|
|
computes its new location as a linear combination of bone transformations:
|
|
|
|
$\mathbf{x}' = \sum\limits_{i = 1}^m w_i(\mathbf{x}) \mathbf{T}_i
|
|
\left(\begin{array}{c}\mathbf{x}_i\\1\end{array}\right),$
|
|
|
|
where $w_i(\mathbf{x})$ is the scalar _weight function_ of the ith bone evaluated at
|
|
$\mathbf{x}$ and $\mathbf{T}_i$ is the bone transformation as a $4 \times 3$
|
|
matrix.
|
|
|
|
This formula is embarassingly parallel (computation at one point does not
|
|
depend on shared data need by computation at another point). It is often
|
|
implemented as a vertex shader. The weights and rest positions for each vertex
|
|
are sent as vertex shader _attributes_ and bone transformations are sent as
|
|
_uniforms_. Then vertices are transformed within the vertex shader, just in
|
|
time for rendering.
|
|
|
|
As the skinning formula is linear (hence its name), we can write it as matrix
|
|
multiplication:
|
|
|
|
$\mathbf{X}' = \mathbf{M} \mathbf{T},$
|
|
|
|
where $\mathbf{X}'$ is $n \times 3$ stack of deformed positions as row
|
|
vectors, $\mathbf{M}$ is a $n \times m\cdot dim$ matrix containing weights and
|
|
rest positions and $\mathbf{T}$ is a $m\cdot (dim+1) \times dim$ stack of
|
|
transposed bone transformations.
|
|
|
|
Traditionally, the weight functions $w_j$ are painted manually by skilled
|
|
rigging professionals. Modern techniques now exist to compute weight functions
|
|
automatically given the shape and a description of the skeleton (or in general
|
|
any handle structure such as a cage, collection of points, selected regions,
|
|
etc.).
|
|
|
|
Bounded biharmonic weights are one such technique that casts weight computation
|
|
as a constrained optimization problem [#jacobson_2011][]. The weights enforce
|
|
smoothness by minimizing the familiar Laplacian energy:
|
|
|
|
$\sum\limits_{i = 1}^m \int_S (\Delta w_i)^2 dA$
|
|
|
|
subject to constraints which enforce interpolation of handle constraints:
|
|
|
|
$w_i(\mathbf{x}) = \begin{cases} 1 & \text{ if } \mathbf{x} \in H_i\\ 0 &
|
|
\text{ otherwise } \end{cases},$
|
|
|
|
where $H_i$ is the ith handle, and constraints which enforce non-negativity,
|
|
parition of unity and encourage sparsity:
|
|
|
|
$0\le w_i \le 1$ and $\sum\limits_{i=1}^m w_i = 1.$
|
|
|
|
This is a quadratic programming problem and libigl solves it using its active
|
|
set solver or by calling out to [Mosek](http://www.mosek.com).
|
|
|
|
 computes weights for a tetrahedral
|
|
mesh given a skeleton (top) and then animates a linear blend skinning
|
|
deformation (bottom).](images/hand-bbw.jpg)
|
|
|
|
## Dual quaternion skinning
|
|
Even with high quality weights, linear blend skinning is limited. In
|
|
particular, it suffers from known artifacts stemming from blending rotations as
|
|
as matrices: a weight combination of rotation matrices is not necessarily a
|
|
rotation. Consider an equal blend between rotating by $-\pi/2$ and by $\pi/2$
|
|
about the $z$-axis. Intuitively one might expect to get the identity matrix,
|
|
but instead the blend is a degenerate matrix scaling the $x$ and $y$
|
|
coordinates by zero:
|
|
|
|
$0.5\left(\begin{array}{ccc}0&-1&0\\1&0&0\\0&0&1\end{array}\right)+
|
|
0.5\left(\begin{array}{ccc}0&1&0\\-1&0&0\\0&0&1\end{array}\right)=
|
|
\left(\begin{array}{ccc}0&0&0\\0&0&0\\0&0&1\end{array}\right)$
|
|
|
|
In practice, this means the shape shrinks and collapses in regions where bone
|
|
weights overlap: near joints.
|
|
|
|
Dual quaternion skinning presents a solution [#kavan_2008]. This method
|
|
represents rigid transformations as a pair of unit quaternions,
|
|
$\hat{\mathbf{q}}$. The linear blend skinning formula is replaced with a
|
|
linear blend of dual quaternions:
|
|
|
|
$\mathbf{x}' =
|
|
\cfrac{\sum\limits_{i=1}^m w_i(\mathbf{x})\hat{\mathbf{q}_i}}
|
|
{\left\|\sum\limits_{i=1}^m w_i(\mathbf{x})\hat{\mathbf{q}_i}\right\|}
|
|
\mathbf{x},$
|
|
|
|
where $\hat{\mathbf{q}_i}$ is the dual quaternion representation of the rigid
|
|
transformation of bone $i$. The normalization forces the result of the linear
|
|
blending to again be a unit dual quaternion and thus also a rigid
|
|
transformation.
|
|
|
|
Like linear blend skinning, dual quaternion skinning is best performed in the
|
|
vertex shader. The only difference being that bone transformations are sent as
|
|
dual quaternions rather than affine transformation matrices. Libigl supports
|
|
CPU-side dual quaternion skinning with the `igl::dqs` function, which takes a
|
|
more traditional representation of rigid transformations as input and
|
|
internally converts to the dual quaternion representation before blending:
|
|
|
|
```cpp
|
|
// vQ is a list of rotations as quaternions
|
|
// vT is a list of translations
|
|
igl::dqs(V,W,vQ,vT,U);
|
|
```
|
|
|
|
 compares linear blend skinning (top) to dual
|
|
quaternion skinning (bottom), highlighting LBS's candy wrapper effect (middle)
|
|
and joint collapse (right).](images/arm-dqs.jpg)
|
|
|
|
## As-rigid-as-possible
|
|
|
|
Skinning and other linear methods for deformation are inherently limited.
|
|
Difficult arises especially when large rotations are imposed by the handle
|
|
constraints.
|
|
|
|
In the context of energy-minimization approaches, the problem stems from
|
|
comparing positions (our displacements) in the coordinate frame of the
|
|
undeformed shape. These quadratic energies are at best invariant to global
|
|
rotations of the entire shape, but not smoothly varying local rotations. Thus
|
|
linear techniques will not produce non-trivial bending and twisting.
|
|
|
|
Furthermore, when considering solid shapes (e.g. discretized with tetrahedral
|
|
meshes) linear methods struggle to maintain local volume, and they often suffer from
|
|
shrinking and bulging artifacts.
|
|
|
|
Non-linear deformation techniques present a solution to these problems.
|
|
They work by comparing the deformation of a mesh
|
|
vertex to its rest position _rotated_ to a new coordinate frame which best
|
|
matches the deformation. The non-linearity stems from the mutual dependence of
|
|
the deformation and the best-fit rotation. These techniques are often labeled
|
|
"as-rigid-as-possible" as they penalize the sum of all local deformations'
|
|
deviations from rotations.
|
|
|
|
To arrive at such an energy, let's consider a simple per-triangle energy:
|
|
|
|
$E_\text{linear}(\mathbf{X}') = \sum\limits_{t \in T} a_t \sum\limits_{\{i,j\}
|
|
\in t} w_{ij} \left\|
|
|
\left(\mathbf{x}'_i - \mathbf{x}'_j\right) -
|
|
\left(\mathbf{x}_i - \mathbf{x}_j\right)\right\|^2$
|
|
|
|
where $\mathbf{X}'$ are the mesh's unknown deformed vertex positions, $t$ is a
|
|
triangle in a list of triangles $T$, $a_t$ is the area of triangle $t$ and
|
|
$\{i,j\}$ is an edge in triangle $t$. Thus, this energy measures the norm of
|
|
change between an edge vector in the original mesh $\left(\mathbf{x}_i -
|
|
\mathbf{x}_j\right)$ and the unknown mesh $\left(\mathbf{x}'_i -
|
|
\mathbf{x}'_j\right)$.
|
|
|
|
This energy is **not** rotation invariant. If we rotate the mesh by 90 degrees
|
|
the change in edge vectors not aligned with the axis of rotation will be large,
|
|
despite the overall deformation being perfectly rigid.
|
|
|
|
So, the "as-rigid-as-possible" solution is to append auxiliary variables
|
|
$\mathbf{R}_t$
|
|
for each triangle $t$ which are constrained to be rotations. Then the energy is
|
|
rewritten, this time comparing deformed edge vectors to their rotated rest
|
|
counterparts:
|
|
|
|
|
|
$E_\text{arap}(\mathbf{X}',\{\mathbf{R}_1,\dots,\mathbf{R}_{|T|}\}) = \sum\limits_{t \in T} a_t \sum\limits_{\{i,j\}
|
|
\in t} w_{ij} \left\|
|
|
\left(\mathbf{x}'_i - \mathbf{x}'_j\right)-
|
|
\mathbf{R}_t\left(\mathbf{x}_i - \mathbf{x}_j\right)\right\|^2.$
|
|
|
|
The separation into the primary vertex position variables $\mathbf{X}'$ and the
|
|
rotations $\{\mathbf{R}_1,\dots,\mathbf{R}_{|T|}\}$ lead to strategy for
|
|
optimization, too. If the rotations $\{\mathbf{R}_1,\dots,\mathbf{R}_{|T|}\}$
|
|
are held fixed then the energy is quadratic in the remaining variables
|
|
$\mathbf{X}'$ and can be optimized by solving a (sparse) global linear system.
|
|
Alternatively, if $\mathbf{X}'$ are held fixed then each rotation is the
|
|
solution to a localized _Procrustes_ problem (found via $3 \times 3$ SVD or
|
|
polar decompostion). These two steps---local and global---each weakly decrease
|
|
the energy, thus we may safely iterate them until convergence.
|
|
|
|
The different flavors of "as-rigid-as-possible" depend on the dimension and
|
|
codimension of the domain and the edge-sets $T$. The proposed surface
|
|
manipulation technique by Sorkine and Alexa [#sorkine_2007][], considers $T$ to
|
|
be the set of sets of edges emanating from each vertex (spokes). Later, Chao et
|
|
al. derived the relationship between "as-rigid-as-possible" mesh energies and
|
|
co-rotational elasticity considering 0-codimension elements as edge-sets:
|
|
triangles in 2D and tetrahedra in 3D [#chao_2010][]. They also showed how
|
|
Sorkine and Alexa's edge-sets are not a discretization of a continuous energy,
|
|
proposing instead edge-sets for surfaces containing all edges of elements
|
|
incident on a vertex (spokes and rims). They show that this amounts to
|
|
measuring bending, albeit in a discretization-dependent way.
|
|
|
|
Libigl, supports these common flavors. Selecting one is a matter of setting the
|
|
energy type before the precompuation phase:
|
|
|
|
```cpp
|
|
igl::ARAPData data;
|
|
arap_data.energy = igl::ARAP_ENERGY_TYPE_SPOKES;
|
|
//arap_data.energy = igl::ARAP_ENERGY_TYPE_SPOKES_AND_RIMS;
|
|
//arap_data.energy = igl::ARAP_ENERGY_TYPE_ELEMENTS; //triangles or tets
|
|
igl::arap_precomputation(V,F,dim,b,data);
|
|
```
|
|
|
|
Just like `igl::min_quad_with_fixed_*`, this precomputation phase only depends
|
|
on the mesh, fixed vertex indices `b` and the energy parameters. To solve with
|
|
certain constraints on the positions of vertices in `b`, we may call:
|
|
|
|
```cpp
|
|
igl::arap_solve(bc,data,U);
|
|
```
|
|
|
|
which uses `U` as an initial guess and then computes the solution into it.
|
|
|
|
Libigl's implementation of as-rigid-as-possible deformation takes advantage of
|
|
the highly optimized singular value decomposition code from McAdams et al.
|
|
[#mcadams_2011][] which leverages SSE intrinsics.
|
|
|
|
 deforms a surface as if it were made of an
|
|
elastic material](images/decimated-knight-arap.jpg)
|
|
|
|
The concept of local rigidity will be revisited shortly in the context of
|
|
surface parameterization.
|
|
|
|
## Fast automatic skinning transformations
|
|
|
|
Non-linear optimization is, unsurprisingly, slower than its linear cousins. In
|
|
the case of the as-rigid-as-possible optimization, the bottleneck is typically
|
|
the large number of polar decompositions necessary to recover best fit
|
|
rotations for each edge-set (i.e. for each triangle, tetrahedron, or vertex
|
|
cell). Even if this code is optimized, the number of primary degrees of freedom
|
|
is tied to the discretization level, despite the deformations' low frequency
|
|
behavior.
|
|
|
|
This invites two routes toward fast non-linear optimization. First, is it
|
|
necessary (or even advantageous) to find so many best-fit rotations? Second,
|
|
can we reduce the degrees of freedom to better reflect the frequency of the
|
|
desired deformations.
|
|
|
|
Taken in turn, these optimizations culminate in a method which optimizes over
|
|
the space of linear blend skinning deformations spanned by high-quality weights
|
|
(i.e. manually painted ones or bounded biharmonic weights). This space is a
|
|
low-dimensional subspace of all possible mesh deformations, captured by writing
|
|
linear blend skinning in matrix form:
|
|
|
|
$\mathbf{X}' = \mathbf{M}\mathbf{T}$
|
|
|
|
where the mesh vertex positions in the $n \times 3$ matrix $\mathbf{X}'$ are
|
|
replaced by a linear combination of a small number of degrees of freedom in the
|
|
$(3+1)m \times 3$ stack of transposed "handle" transformations. Swapping in
|
|
$\mathbf{M}\mathbf{T}$ for $\mathbf{X}'$ in the ARAP energies above immediately
|
|
sees performance gains during the global solve step as $m << n$.
|
|
|
|
The complexity of the local step---fitting rotations---is still bound
|
|
to the original mesh discretization. However, if the skinning is well behaved,
|
|
we can make the assumption that places on the shape with similar skinning
|
|
weights will deform similarly and thus imply similar best-fit rotations.
|
|
Therefore, we cluster edge-sets according to their representation in
|
|
_weight-space_: where a vertex $\mathbf{x}$ takes the coordinates
|
|
$[w_1(\mathbf{x}),w_2(\mathbf{x}),\dots,w_m(\mathbf{x})]$. The number of
|
|
clustered edge-sets show diminishing returns on the deformation quality so we
|
|
may choose a small number of clusters, proportional to the number of skinning
|
|
weight functions (rather than the number of discrete mesh vertices).
|
|
|
|
This proposed deformation model [#jacobson_2012][], can simultaneously be seen as a
|
|
fast, subspace optimization for ARAP and as an automatic method for finding
|
|
_the best_ skinning transformation degrees of freedom.
|
|
|
|
A variety of user interfaces are supported via linear equality constraints on
|
|
the skinning transformations associated with handles. To fix a transformation
|
|
entirely we simply add the constraint:
|
|
|
|
$\left(\begin{array}{cccc}
|
|
1 & 0 & 0 & 0\\
|
|
0 & 1 & 0 & 0\\
|
|
0 & 0 & 1 & 0\\
|
|
0 & 0 & 0 & 1\end{array}\right)
|
|
\mathbf{T}_i^T = \hat{\mathbf{T}}_i^T,$
|
|
|
|
where $\hat{\mathbf{T}}_i^T$ is the $(3+1) \times 3$ transposed fixed
|
|
transformation for handle $i$.
|
|
|
|
To fix only the origin of a handle, we add a constraint requiring the
|
|
transformation to interpolate a point in space (typically the centroid of all
|
|
points with $w_i = 1$:
|
|
|
|
$\mathbf{c}'^T\mathbf{T}_i^T = \mathbf{c}^T,$
|
|
|
|
where $\mathbf{c}^T$ is the $1 \times (3+1)$ position of the point at rest in
|
|
transposed homogeneous coordinates, and $\mathbf{c}'^T$ the point given by the
|
|
user.
|
|
|
|
We can similarly fix just the linear part of the transformation at a handle,
|
|
freeing the translation component (producing a "chickenhead" effect):
|
|
|
|
$\left(\begin{array}{cccc}
|
|
1&0&0&0\\
|
|
0&1&0&0\\
|
|
0&0&1&0\end{array}\right)
|
|
\mathbf{T}_i^T = \hat{\mathbf{L}}_i^T,$
|
|
|
|
where $\hat{\mathbf{L}}_i^T$ is the fixed $3 \times 3$ linear part of the
|
|
transformation at handle $i$.
|
|
|
|
And lastly we can allow the user to entirely _free_ the transformation's
|
|
degrees of freedom, delegating the optimization to find the best possible
|
|
values for all elements. To do this, we simply abstain from adding a
|
|
corresponding constraint.
|
|
|
|
### ARAP with grouped edge-sets
|
|
|
|
Being a subspace method, an immediate disadvantage is the reduced degrees of
|
|
freedom. This brings performance, but in some situations limits behavior too
|
|
much. In such cases one can use the skinning subspace to build an effective
|
|
clustering of rotation edge-sets for a traditional ARAP optimization: forgoing
|
|
the subspace substitution. This has an two-fold effect. The cost of the
|
|
rotation fitting, local step drastically reduces, and the deformations are
|
|
"regularized" according the clusters. From a high level point of view, if the
|
|
clusters are derived from skinning weights, then they will discourage bending,
|
|
especially along isolines of the weight functions. If handles are not known in
|
|
advance, one could also cluster according to a "geodesic embedding" like the
|
|
biharmonic distance embedding.
|
|
|
|
In this light, we can think of the "spokes+rims" style surface ARAP as a (slight and
|
|
redundant) clustering of the per-triangle edge-sets.
|
|
|
|
 compares a full (slow)
|
|
ARAP deformation on a detailed shape (left of middle), to ARAP with grouped
|
|
rotation edge sets (right of middle), to the very fast subpsace method
|
|
(right).](images/armadillo-fast.jpg)
|
|
|
|
## Biharmonic Coordinates
|
|
|
|
Linear blend skinning (as [above](#boundedbiharmonicweights)) deforms a mesh by
|
|
propogating _full affine transformations_ at handles (bones, points, regions,
|
|
etc.) to the rest of the shape via weights. Another deformation framework,
|
|
called "generalized barycentric coordinates", is a special case of linear blend
|
|
skinning [#jacobson_skinning_course_2014][]: transformations are restricted to
|
|
_pure translations_ and weights are required to retain _affine precision_. This
|
|
latter requirement means that we can write the rest-position of any vertex in
|
|
the mesh as the weighted combination of the control handle locations:
|
|
|
|
$\mathbf{x} = \sum\limits_{i=1}^m w_i(\mathbf{x}) * \mathbf{c}_i,$
|
|
|
|
where $\mathbf{c}_i$ is the rest position of the $i$th control point. This
|
|
simplifies the deformation formula at run-time. We can simply take the new
|
|
position of each point of the shape to be the weighted combination of the
|
|
_translated_ control point positions:
|
|
|
|
$\mathbf{x}' = \sum\limits_{i=1}^m w_i(\mathbf{x}) * \mathbf{c}_i'.$
|
|
|
|
There are _many_ different flavors of "generalized barycentric coordinates"
|
|
(see table in "Automatic Methods" section,
|
|
[#jacobson_skinning_course_2014][]). The vague goal of "generalized barycentric
|
|
coordinates" is to capture as many properties of simplicial barycentric
|
|
coordinates (e.g. for triangles in 2D and tetrahedral in 3D) for larger sets of
|
|
points or polyhedra. Some generalized barycentric coordinates can be computed
|
|
in closed form; others require optimization-based precomputation. Nearly all
|
|
flavors require connectivity information describing how the control points form
|
|
a external polyhedron around the input shape: a cage. However, a recent
|
|
techinique does not require a cage [#wang_bc_2015][]. This method ensures
|
|
affine precision during optimization over weights of a smoothness energy with
|
|
affine functions in its kernel:
|
|
|
|
$\mathop{\text{min}}_\mathbf{W}\,\, \text{trace}(\frac{1}{2}\mathbf{W}^T \mathbf{A}
|
|
\mathbf{W}), \text{subject to: } \mathbf{C} = \mathbf{W}\mathbf{C}$
|
|
|
|
subject to interpolation constraints at selected vertices. If $\mathbf{A}$ has
|
|
affine functions in its kernel---that is, if $\mathbf{A}\mathbf{V} = 0$---then
|
|
the weights $\mathbf{W}$ will retain affine precision and we'll have that:
|
|
|
|
$\mathbf{V} = \mathbf{W}\mathbf{C}$
|
|
|
|
the matrix form of the equality above. The proposed way to define $\mathbf{A}$
|
|
is to construct a matrix $\mathbf{K}$ that measures the Laplacian at all
|
|
interior vertices _and at all boundary vertices_. The _usual_ definition of the
|
|
discrete Laplacian (e.g. what libigl returns from `igl::cotmatrix`), measures
|
|
the Laplacian of a function for interior vertices, but measures the Laplacian
|
|
of a function _minus_ the normal derivative of a function for boundary
|
|
vertices. Thus, we can let:
|
|
|
|
$\mathbf{K} = \mathbf{L} + \mathbf{N}$
|
|
|
|
where $\mathbf{L}$ is the _usual_ Laplacian and $\mathbf{N}$ is matrix that
|
|
computes normal derivatives of a piecewise-linear function at boundary vertices
|
|
of a mesh. Then $\mathbf{A}$ is taken as quadratic form computing the square of
|
|
the integral-average of $\mathbf{K}$ applied to a function and integrated over
|
|
the mesh:
|
|
|
|
$\mathbf{A} = (\mathbf{M}^{-1}\mathbf{K})^2_\mathbf{M} = \mathbf{K}^T \mathbf{M}^{-1}
|
|
\mathbf{K}.$
|
|
|
|
Since the Laplacian $\mathbf{K}$ is a second-order derivative it measures zero on affine
|
|
functions, thus $\mathbf{A}$ has affine functions in its null space. A short
|
|
derivation proves that this implies $\mathbf{W}$ will be affine precise (see
|
|
[#wang_bc_2015][]).
|
|
|
|
Minimizers of this "squared Laplacian" energy are in some sense _discrete
|
|
biharmonic functions_. Thus they're dubbed "biharmonic coordinates" (not the
|
|
same as _bounded biharmonic weights_, which are _not_ generalized barycentric
|
|
coordinates).
|
|
|
|
In libigl, one can compute biharmonic coordinates given a mesh `(V,F)` and a
|
|
list `S` of selected control points or control regions (which act like skinning
|
|
handles):
|
|
|
|
```cpp
|
|
igl::biharmonic_coordinates(V,F,S,W);
|
|
```
|
|
|
|
) shows a physics
|
|
simulation on a coarse orange mesh. The vertices of this mesh become control
|
|
points for a biharmonic coordinates deformation of the blue high-resolution
|
|
mesh.](images/octopus-biharmonic-coordinates-physics.gif)
|
|
|
|
|
|
# Chapter 5: Parametrization [chapter5:parametrization]
|
|
|
|
In computer graphics, we denote as surface parametrization a map from the
|
|
surface to \\(\mathbf{R}^2\\). It is usually encoded by a new set of 2D
|
|
coordinates for each vertex of the mesh (and possibly also by a new set of
|
|
faces in one to one correspondence with the faces of the original surface).
|
|
Note that
|
|
this definition is the *inverse* of the classical differential geometry
|
|
definition.
|
|
|
|
A parametrization has many applications, ranging from texture mapping to
|
|
surface remeshing. Many algorithms have been proposed, and they can be broadly
|
|
divided in four families:
|
|
|
|
1. **Single patch, fixed boundary**: these algorithm can parametrize a
|
|
disk-like part of the surface given fixed 2D positions for its boundary. These
|
|
algorithms are efficient and simple, but they usually produce high-distortion maps due to the fixed boundary.
|
|
|
|
2. **Single patch, free boundary:** these algorithms let the boundary
|
|
deform freely, greatly reducing the map distortion. Care should be taken to
|
|
prevent the border to self-intersect.
|
|
|
|
3. **Global parametrization**: these algorithms work on meshes with arbitrary
|
|
genus. They initially cut the mesh in multiple patches that can be separately parametrized. The generated maps are discontinuous on the cuts (often referred as *seams*).
|
|
|
|
4. **Global seamless parametrization**: these are global parametrization algorithm that hides the seams, making the parametrization "continuous", under specific assumptions that we will discuss later.
|
|
|
|
## Harmonic parametrization [harmonicparametrization]
|
|
|
|
Harmonic parametrization [#eck_2005][] is a single patch, fixed boundary parametrization
|
|
algorithm that computes the 2D coordinates of the flattened mesh as two
|
|
harmonic functions.
|
|
|
|
The algorithm is divided in 3 steps:
|
|
|
|
1. Detect of the boundary vertices
|
|
2. Map the boundary vertices to a circle
|
|
3. Compute two harmonic functions (one for u and one for the v coordinate). The harmonic functions use the fixed vertices on the circle as boundary constraints.
|
|
|
|
The algorithm can be coded using libigl as follows:
|
|
|
|
```cpp
|
|
Eigen::VectorXi bnd;
|
|
igl::boundary_loop(V,F,bnd);
|
|
|
|
Eigen::MatrixXd bnd_uv;
|
|
igl::map_vertices_to_circle(V,bnd,bnd_uv);
|
|
|
|
igl::harmonic(V,F,bnd,bnd_uv,1,V_uv);
|
|
```
|
|
|
|
where `bnd` contains the indices of the boundary vertices, bnd_uv their position on the UV plane, and "1" denotes that we want to compute an harmonic function (2 will be for biharmonic, 3 for triharmonic, etc.). Note that each of the three
|
|
functions is designed to be reusable in other parametrization algorithms.
|
|
|
|
A UV parametrization can be visualized in the viewer with:
|
|
|
|
```cpp
|
|
viewer.data.set_uv(V_uv);
|
|
```
|
|
|
|
The UV coordinates are then used to apply a procedural checkerboard texture to the
|
|
mesh ([Example 501](501_HarmonicParam/main.cpp)).
|
|
|
|
) Harmonic parametrization. (left)
|
|
mesh with texture, (right) UV parametrization with
|
|
texture](images/501_HarmonicParam.png)
|
|
|
|
## Least squares conformal maps [leastsquareconformalmaps]
|
|
|
|
Least squares conformal maps parametrization [#levy_2002][] minimizes the
|
|
conformal (angular) distortion of the parametrization. Differently from
|
|
harmonic parametrization, it does not need to have a fixed boundary.
|
|
|
|
LSCM minimizes the following energy:
|
|
|
|
\\[ E_{LSCM}(\mathbf{u},\mathbf{v}) = \int_X \frac{1}{2}| \nabla \mathbf{u}^{\perp} - \nabla \mathbf{v} |^2 dA \\]
|
|
|
|
which can be rewritten in matrix form as [#mullen_2008][]:
|
|
|
|
\\[ E_{LSCM}(\mathbf{u},\mathbf{v}) = \frac{1}{2} [\mathbf{u},\mathbf{v}]^t (L_c - 2A) [\mathbf{u},\mathbf{v}] \\]
|
|
|
|
where $L_c$ is the cotangent Laplacian matrix and A is a matrix such that \\(
|
|
[\mathbf{u},\mathbf{v}]^t A [\mathbf{u},\mathbf{v}]$ is equal to the
|
|
[vector area](http://en.wikipedia.org/wiki/Vector_area) of the mesh.
|
|
|
|
Using libigl, this matrix energy can be written in a few lines of codes. The
|
|
cotangent matrix can be computed using `igl::cotmatrix`:
|
|
|
|
```cpp
|
|
SparseMatrix<double> L;
|
|
igl::cotmatrix(V,F,L);
|
|
```
|
|
|
|
Note that we want to apply the Laplacian matrix to the u and v coordinates at
|
|
the same time, thus we need to extend it taking the left
|
|
Kronecker product with a 2x2 identity matrix:
|
|
|
|
```cpp
|
|
SparseMatrix<double> L_flat;
|
|
igl::repdiag(L,2,L_flat);
|
|
```
|
|
|
|
The area matrix is computed with `igl::vector_area_matrix`:
|
|
|
|
```cpp
|
|
SparseMatrix<double> A;
|
|
igl::vector_area_matrix(F,A);
|
|
```
|
|
|
|
The final energy matrix is the sum of these two matrices. Note that in this
|
|
case we do not need to fix the boundary. To remove the null space of the energy and make the minimum unique, it is sufficient to fix two arbitrary
|
|
vertices to two arbitrary positions. The full source code is provided in [Example 502](502_LSCMParam/main.cpp).
|
|
|
|
|
|
) LSCM parametrization. (left) mesh
|
|
with texture, (right) UV parametrization](images/502_LSCMParam.png)
|
|
|
|
## As-rigid-as-possible parametrization [asrigidaspossible]
|
|
|
|
As-rigid-as-possible parametrization [#liu_2008][] is a powerful single-patch,
|
|
non-linear algorithm to compute a parametrization that strives to preserve
|
|
distances (and thus angles). The idea is very similar to ARAP surface
|
|
deformation: each triangle is mapped to the plane trying to preserve its
|
|
original shape, up to a rigid rotation.
|
|
|
|
The algorithm can be implemented reusing the functions discussed in the
|
|
deformation chapter: `igl::arap_precomputation` and `igl::arap_solve`. The only
|
|
difference is that the optimization has to be done in 2D instead of 3D and that
|
|
we need to compute a starting point. While for 3D deformation the optimization
|
|
is bootstrapped with the original mesh, this is not the case for ARAP
|
|
parametrization since the starting point must be a 2D mesh. In [Example
|
|
503](503_ARAPParam/main.cpp), we initialize the optimization with harmonic
|
|
parametrization. Similarly to LSCM, the boundary is free to deform to minimize
|
|
the distortion.
|
|
|
|
) As-Rigid-As-Possible parametrization.
|
|
(left) mesh with texture, (right) UV parametrization with
|
|
texture](images/503_ARAPParam.png)
|
|
|
|
## N-rotationally symmetric tangent fields [nrotationallysymmetrictangetfields]
|
|
|
|
The design of tangent fields is a basic tool used to design guidance fields for
|
|
uniform quadrilateral and hexahedral remeshing. Libigl contains an
|
|
implementation of all the state-of-the-art algorithms to design N-RoSy fields
|
|
and their generalizations.
|
|
|
|
In libigl, tangent unit-length vector fields are piece-wise constant on the
|
|
faces of a triangle mesh, and they are described by one or more vectors per-face. The function
|
|
|
|
```cpp
|
|
igl::nrosy(V,F,b,bc,b_soft,b_soft_weight,bc_soft,N,0.5,
|
|
output_field,output_singularities);
|
|
```
|
|
|
|
creates a smooth unit-length vector field (N=1) starting from a sparse set of
|
|
constrained faces, whose indices are listed in b and their constrained value is
|
|
specified in bc. The functions supports soft_constraints (b_soft,
|
|
b_soft_weight, bc_soft), and returns the interpolated field for each face of
|
|
the triangle mesh (output_field), plus the singularities of the field
|
|
(output_singularities).
|
|
|
|
|
|
|
|
The singularities are vertices where the field vanishes (highlighted in red in
|
|
the figure above). `igl::nrosy` can also generate N-RoSy fields [#levy_2008][],
|
|
which are a generalization of vector fields where in every face the vector is
|
|
defined up to a constant rotation of $2\pi / N$. As can be observed in
|
|
the following figure, the singularities of the fields generated with different
|
|
N are of different types and they appear in different positions.
|
|
|
|
|
|
|
|
We demonstrate how to call and plot N-RoSy fields in [Example
|
|
504](504_NRosyDesign/main.cpp), where the degree of the field can be change
|
|
pressing the number keys. `igl::nrosy` implements the algorithm proposed in
|
|
[#bommes_2009][]. N-RoSy fields can also be interpolated with the algorithm
|
|
proposed in [#knoppel_2013][], see Section [npolyvectorfields] for more details
|
|
([igl::n_polyvector](../include/igl/n_polyvector.h)).
|
|
|
|
### Global, seamless integer-grid parametrization [globalseamlessintegergridparametrization]
|
|
|
|
The previous parametrization methods were focusing on creating parametrizations
|
|
of surface patches aimed at texture mapping or baking of other surface
|
|
properties such as normals and high-frequency details. Global, seamless
|
|
parametrization aims at parametrizing complex shapes with a parametrization
|
|
that is aligned with a given set of directions for the purpose of surface
|
|
remeshing. In libigl, we provide a reference implementation of the pipeline
|
|
proposed in the mixed integer quadrangulation paper [#bommes_2009][].
|
|
|
|
The first step involves the design of a 4-RoSy field (sometimes called *cross*
|
|
field) that describes the alignment of the edges of the desired quadrilateral
|
|
remeshing. The field constraints are usually manually specified or extracted
|
|
from the principal curvature directions. In [[Example
|
|
506](506_FrameField/main.cpp)], we simply fix one face in a random direction.
|
|
|
|
|
|
|
|
### Combing and cutting
|
|
|
|
Given the cross field, we now want to cut the surface so that it becomes
|
|
homeomorphic to a disk. While this could be done directly on the cross-field, we
|
|
opt to perform this operation on its bisector field (a copy of the field
|
|
rotated by 45 degrees) since it is more stable and generic. Working on the
|
|
bisectors allow us to take as input generalized, non-orthogonal and non-unit
|
|
length cross fields.
|
|
|
|
We thus rotate the field,
|
|
|
|
|
|
|
|
and we remove the rotation ambiguity by assigning to each face a u and a v
|
|
direction. The assignment is done with a breadth-first search starting from a
|
|
random face.
|
|
|
|
|
|
|
|
You can imagine this process as combing an hairy surface: you will be able to
|
|
comb part of it, but at some point you will not be able to consistently comb
|
|
the entire surface ([Hairy ball
|
|
theorem](http://en.wikipedia.org/wiki/Hairy_ball_theorem)). The discontinuities
|
|
in the combing define the cut graph:
|
|
|
|
|
|
|
|
Finally, we rotate the combed field by 45 degrees to undo the initial degrees
|
|
rotation:
|
|
|
|
|
|
|
|
The combed cross field can be seen as the ideal Jacobian of the parametrization
|
|
that will be computed in the next section.
|
|
|
|
### Poisson parametrization
|
|
|
|
The mesh is cut along the seams and a parametrization is computed trying to
|
|
find two scalar functions whose gradient matches the combed cross field
|
|
directions. This is a classical Poisson problem, that is solved minimizing the
|
|
following quadratic energy:
|
|
|
|
\\[ E(\mathbf{u},\mathbf{v}) = |\nabla \mathbf{u} - X_u|^2 + |\nabla \mathbf{v} - X_v|^2 \\]
|
|
|
|
where $X_u$ and $X_u$ denotes the combed cross field. Solving this
|
|
problem generates a parametrization whose u and v isolines are aligned with the
|
|
input cross field.
|
|
|
|
|
|
|
|
We hide the seams by adding integer constraints to the Poisson problem
|
|
that align the isolines on both sides of each seam [#bommes_2009].
|
|
|
|
|
|
|
|
Note that this parametrization can only be used for remeshing purposes, since
|
|
it contains many overlaps.
|
|
|
|
|
|
|
|
A quad mesh can be extracted from this parametrization using
|
|
[libQEx](https://github.com/hcebke/libQEx) (not included in libigl).
|
|
The full pipeline is implemented in [Example 505](505_MIQ/main.cpp).
|
|
|
|
## Anisotropic remeshing [anisotropicremeshingusingframefields]
|
|
|
|
Anisotropic and non-uniform quad remeshing is important to concentrate the
|
|
elements in the regions with more details. It is possible to extend the MIQ
|
|
quad meshing framework to generate anisotropic quad meshes using a mesh
|
|
deformation approach [#panozzo_2014][].
|
|
|
|
The input of the anisotropic remeshing algorithm is a sparse set of constraints
|
|
that define the shape and scale of the desired quads. This can be encoded as a
|
|
frame field, which is a pair of non-orthogonal and non-unit length vectors. The
|
|
frame field can be interpolated by decomposing it in a 4-RoSy field and a
|
|
unique affine transformation. The two parts can then be interpolated
|
|
separately, using `igl::nrosy` for the cross field, and an harmonic interpolant
|
|
for the affine part.
|
|
|
|
|
|
|
|
After the interpolation, the surface is warped to transform each frame into an
|
|
orthogonal and unit length cross (i.e. removing the scaling and skewness from
|
|
the frame). This deformation defines a new embedding (and a new metric) for the
|
|
surface.
|
|
|
|
|
|
|
|
The deformed surface can the be isotropically remeshed using the MIQ algorithm
|
|
that has been presented in the previous section.
|
|
|
|
|
|
|
|
The UV coordinates of the deformed surface can then be used to transport the
|
|
parametrization to the original surface, where the isolines will trace a quad
|
|
mesh whose elements are similar to the shape prescribed in the input frame
|
|
field.
|
|
|
|
|
|
|
|
Our implementation ([Example 506](506_FrameField/main.cpp)) uses MIQ to
|
|
generate the UV parametrization, but other algorithms could be applied: the
|
|
only desiderata is that the generated quad mesh should be as isotropic as
|
|
possible.
|
|
|
|
## N-PolyVector fields [npolyvectorfields]
|
|
|
|
N-RoSy vector fields can be further generalized to represent arbitrary
|
|
vector-sets, with arbitrary angles between them and with arbitrary lengths
|
|
[#diamanti_2014][]. This generalization is called N-PolyVector field, and
|
|
libigl provides the function `igl::n_polyvector` to design them starting from a
|
|
sparse set of constraints ([Example 507](507_PolyVectorField/main.cpp)).
|
|
|
|
|
|
|
|
The core idea is to represent the vector set as the roots of a complex
|
|
polynomial: The polynomial coefficients are then harmonically interpolated
|
|
leading to polynomials whose roots smoothly vary over the surface.
|
|
|
|
Globally optimal direction fields [#knoppel_2013][] are a special case of
|
|
PolyVector fields. If the constraints are taken from an N-RoSy field,
|
|
`igl::n_polyvector` generates a field that is equivalent, after normalization,
|
|
to a globally optimal direction field.
|
|
|
|
## Conjugate vector fields [conjugatevectorfields]
|
|
|
|
Two tangent vectors lying on a face of a triangle mesh are conjugate if
|
|
|
|
\\[ k_1 (u^T d_1)(v^T d_1) + k_2(u^T d_2)(v^T d_2) = 0. \\]
|
|
|
|
This condition is very important in architectural geometry: The faces of an
|
|
infinitely dense quad mesh whose edges are aligned with a conjugate field are
|
|
planar. Thus, a quad mesh whose edges follow a conjugate field are easier to
|
|
planarize [#liu_2011].
|
|
|
|
Finding a conjugate vector field that satisfies given directional constraints
|
|
is a standard problem in architectural geometry, which can be tackled by
|
|
deforming a Poly-Vector field to the closest conjugate field.
|
|
|
|
This algorithm [#diamanti_2014] alternates a global step, which enforces
|
|
smoothness, with a local step, that projects the field on every face to the
|
|
closest conjugate field ([Example 508](508_ConjugateField/main.cpp)).
|
|
|
|
|
|
|
|
## Planarization [planarization]
|
|
|
|
A quad mesh can be transformed in a planar quad mesh with Shape-Up
|
|
[#bouaziz_2012], a local/global approach that uses the global step to enforce
|
|
surface continuity and the local step to enforce planarity.
|
|
|
|
[Example 509](509_Planarization/main.cpp) planarizes a quad mesh until it
|
|
satisfies a user-given planarity threshold.
|
|
|
|
|
|
|
|
# Chapter 6: External libraries [chapter6:externallibraries]
|
|
|
|
An additional positive side effect of using matrices as basic types is that it
|
|
is easy to exchange data between libigl and other softwares and libraries.
|
|
|
|
## State serialization [stateserialization]
|
|
|
|
Geometry processing applications often require a considerable amount of
|
|
computational time and/or manual input. Serializing the state of the application is a simple strategy to greatly increase the development efficiency. It allows to quickly start debugging just
|
|
before the crash happens, avoiding to wait for the precomputation to take place
|
|
every time and it also makes your experiments reproducible, allowing to quickly test algorithms variants on the same input data.
|
|
|
|
Serialization is often not considered in geometry processing due
|
|
to the extreme difficulty in serializing pointer-based data structured, such as
|
|
an half-edge data structure ([OpenMesh](http://openmesh.org), [CGAL](http://www.cgal.org)), or a pointer based indexed structure ([VCG](http://vcg.isti.cnr.it/~cignoni/newvcglib/html/)).
|
|
|
|
In libigl, serialization is much simpler, since the majority of the functions use basic types, and pointers are used in very rare cases (usually to interface
|
|
with external libraries). Libigl bundles a simple and self-contained binary and XML serialization framework, that drastically reduces the overhead required to add
|
|
serialization to your applications.
|
|
|
|
To de-/serialize a set of variables use the following method:
|
|
|
|
```cpp
|
|
#include "igl/serialize.h"
|
|
|
|
bool b = true;
|
|
unsigned int num = 10;
|
|
std::vector<float> vec = {0.1,0.002,5.3};
|
|
|
|
// use overwrite = true for the first serialization to create or overwrite an existing file
|
|
igl::serialize(b,"B","filename",true);
|
|
// append following serialization to existing file
|
|
igl::serialize(num,"Number","filename");
|
|
igl::serialize(vec,"VectorName","filename");
|
|
|
|
// deserialize back to variables
|
|
igl::deserialize(b,"B","filename");
|
|
igl::deserialize(num,"Number","filename");
|
|
igl::deserialize(vec,"VectorName","filename");
|
|
```
|
|
|
|
Currently all fundamental data types (bool, int, float, double, ...) are supported, as well as std::string, basic `STL` containers, dense and sparse Eigen matrices and nestings of those.
|
|
Some limitations apply to pointers. Currently, loops or many to one type of link structures are not handled correctly. Each pointer is assumed to point to a different independent object.
|
|
Uninitialized pointers must be set to `nullptr` before de-/serialization to avoid memory leaks. Cross-platform issues like little-, big-endianess is currently not supported.
|
|
To make user defined types serializable, just derive from `igl::Serializable` and trivially implementing the `InitSerialization` method.
|
|
|
|
Assume that the state of your application is a mesh and a set of integer ids:
|
|
|
|
```cpp
|
|
#include "igl/serialize.h"
|
|
|
|
struct State : public igl::Serializable
|
|
{
|
|
Eigen::MatrixXd V;
|
|
Eigen::MatrixXi F;
|
|
std::vector<int> ids;
|
|
|
|
void InitSerialization()
|
|
{
|
|
this->Add(V , "V");
|
|
this->Add(F , "F");
|
|
this->Add(ids, "ids");
|
|
}
|
|
};
|
|
```
|
|
|
|
If you need more control over the serialization of your types, you can override the following functions or directly inherit from the interface `igl::SerializableBase`.
|
|
|
|
```cpp
|
|
bool Serializable::PreSerialization() const;
|
|
void Serializable::PostSerialization() const;
|
|
bool Serializable::PreDeserialization();
|
|
void Serializable::PostDeserialization();
|
|
```
|
|
|
|
Alternatively, if you want a non-intrusive way of serializing your state you can overload the following functions:
|
|
|
|
```cpp
|
|
namespace igl { namespace serialization {
|
|
|
|
void serialize(const State& obj,std::vector<char>& buffer){
|
|
::igl::serialize(obj.V,std::string("V"),buffer);
|
|
::igl::serialize(obj.F,std::string("F"),buffer);
|
|
::igl::serialize(obj.ids,std::string("ids"),buffer);
|
|
}
|
|
void deserialize(State& obj,const std::vector<char>& buffer){
|
|
::igl::deserialize(obj.V,std::string("V"),buffer);
|
|
::igl::deserialize(obj.F,std::string("F"),buffer);
|
|
::igl::deserialize(obj.ids,std::string("ids"),buffer);
|
|
}
|
|
}}
|
|
```
|
|
|
|
Equivalently, you can use the following macros:
|
|
|
|
```cpp
|
|
SERIALIZE_TYPE(State,
|
|
SERIALIZE_MEMBER(V)
|
|
SERIALIZE_MEMBER(F)
|
|
SERIALIZE_MEMBER_NAME(ids,"ids")
|
|
)
|
|
```
|
|
|
|
All the former code is for binary serialization which is especially useful if you have to handle larger data where the loading and saving times become more important.
|
|
For cases where you want to read and edit the serialized data by hand we provide a serialization to XML files which is based on the library [tinyxml2](https://github.com/leethomason/tinyxml2).
|
|
There you also have the option to create a partial binary serialization of your data by using the binary parameter, exposed in the function `serialize_xml()`:
|
|
|
|
```cpp
|
|
#include "igl/xml/serialize_xml.h"
|
|
|
|
int number;
|
|
|
|
// binary = false, overwrite = true
|
|
igl::serialize_xml(vec,"VectorXML",xmlFile,false,true);
|
|
// binary = true, overwrite = true
|
|
igl::serialize_xml(vec,"VectorBin",xmlFile,true,true);
|
|
igl::deserialize_xml(vec,"VectorXML",xmlFile);
|
|
igl::deserialize_xml(vec,"VectorBin",xmlFile);
|
|
```
|
|
|
|
For user defined types derive from `XMLSerializable`.
|
|
|
|
The code snippets above are extracted from [Example
|
|
601](601_Serialization/main.cpp). We strongly suggest that you make the entire
|
|
state of your application always serializable since it will save you a lot of
|
|
troubles when you will be preparing figures for a scientific report. It is very
|
|
common to have to do small changes to figures, and being able to serialize the
|
|
entire state just before you take screenshots will save you many painful hours
|
|
before a submission deadline.
|
|
|
|
## Mixing Matlab code [mixingmatlabcode]
|
|
|
|
Libigl can be interfaced with Matlab to offload numerically heavy computation
|
|
to a Matlab script. The major advantage of this approach is that you will be
|
|
able to develop efficient and complex user-interfaces in C++, while exploting
|
|
the syntax and fast protototyping features of matlab. In particular, the use of
|
|
an external Matlab script in a libigl application allows to change the Matlab
|
|
code while the C++ application is running, greatly increasing coding
|
|
efficiency.
|
|
|
|
We demonstrate how to integrate Matlab in a libigl application in [Example
|
|
602](602_Matlab/main.cpp). The example uses Matlab to compute the
|
|
Eigenfunctions of the discrete Laplacian operator, relying on libigl for mesh
|
|
IO, visualization and for computing the Laplacian operator.
|
|
|
|
Libigl can connect to an existing instance of Matlab (or launching a new one on
|
|
Linux/MacOSX) using:
|
|
|
|
```cpp
|
|
igl::mlinit(&engine);
|
|
```
|
|
|
|
The cotangent Laplacian is computed using igl::cotmatrix and uploaded to the
|
|
Matlab workspace:
|
|
|
|
```cpp
|
|
igl::cotmatrix(V,F,L);
|
|
igl::mlsetmatrix(&engine,"L",L);
|
|
```
|
|
|
|
It is now possible to use any Matlab function on the data. For example, we can
|
|
see the sparsity pattern of L using spy:
|
|
|
|
```cpp
|
|
igl::mleval(&engine,"spy(L)");
|
|
```
|
|
|
|
|
|
|
|
The results of Matlab computations can be returned back to the C++ application
|
|
|
|
```cpp
|
|
igl::mleval(&engine,"[EV,~] = eigs(-L,10,'sm')");
|
|
igl::mlgetmatrix(&engine,"EV",EV);
|
|
```
|
|
|
|
and plotted using the libigl viewer.
|
|
|
|
|
|
|
|
|
|
### Saving a Matlab workspace
|
|
To aid debugging, libigl also supplies functions to write Matlab `.mat`
|
|
"Workspaces". This C++ snippet saves a mesh and it's sparse Laplacian matrix to
|
|
a file:
|
|
|
|
```cpp
|
|
igl::readOFF("../shared/fertility.off", V, F);
|
|
igl::cotmatrix(V,F,L);
|
|
igl::MatlabWorkspace mw;
|
|
mw.save(V,"V");
|
|
mw.save_index(F,"F");
|
|
mw.save(L,"L");
|
|
mw.write("fertility.mat");
|
|
```
|
|
|
|
Then this workspace can be loaded into a Matlab IDE:
|
|
|
|
```matlab
|
|
load fertility.mat
|
|
```
|
|
|
|
The `igl::MatlabWorkspace` depends on Matlab libraries to compile and run,
|
|
but---in contrast to the engine routines above---will avoid launching a Matlab
|
|
instance upon execution.
|
|
|
|
### Dumping Eigen matrices to copy and paste into Matlab
|
|
Eigen supplies a sophisticated API for printing its matrix types to the screen.
|
|
Libigl has wrapped up a particularly useful formatting which makes it simple to
|
|
copy standard output from a C++ program into a Matlab IDE. The code:
|
|
|
|
```cpp
|
|
igl::readOFF("../shared/2triangles.off", V, F);
|
|
igl::cotmatrix(V,F,L);
|
|
std::cout<<igl::matlab_format(V,"V")<<std::endl;
|
|
std::cout<<igl::matlab_format((F.array()+1).eval(),"F")<<std::endl;
|
|
std::cout<<igl::matlab_format(L,"L")<<std::endl;
|
|
```
|
|
|
|
produces the output:
|
|
|
|
```matlab
|
|
V = [
|
|
0 0 0
|
|
1 0 0
|
|
1 1 1
|
|
2 1 0
|
|
];
|
|
F = [
|
|
1 2 3
|
|
2 4 3
|
|
];
|
|
LIJV = [
|
|
1 1 -0.7071067811865476
|
|
2 1 0.7071067811865475
|
|
3 1 1.570092458683775e-16
|
|
1 2 0.7071067811865475
|
|
2 2 -1.638010440969447
|
|
3 2 0.6422285251880865
|
|
4 2 0.2886751345948129
|
|
1 3 1.570092458683775e-16
|
|
2 3 0.6422285251880865
|
|
3 3 -0.9309036597828995
|
|
4 3 0.2886751345948129
|
|
2 4 0.2886751345948129
|
|
3 4 0.2886751345948129
|
|
4 4 -0.5773502691896258
|
|
];
|
|
L = sparse(LIJV(:,1),LIJV(:,2),LIJV(:,3));
|
|
```
|
|
|
|
which is easily copied and pasted into Matlab for debugging, etc.
|
|
|
|
## Calling libigl functions from Matlab [callinglibiglfunctionsfrommatlab]
|
|
|
|
It is also possible to call libigl functions from matlab, compiling them as MEX
|
|
functions. This can be used to offload to C++ code the computationally
|
|
intensive parts of a Matlab application.
|
|
|
|
We provide a wrapper for `igl::readOBJ` in [Example 603](603_MEX/compileMEX.m).
|
|
We plan to provide wrappers for all our functions in the future, if you are
|
|
interested in this feature (or if you want to help implementing it) please let
|
|
us know.
|
|
|
|
## Triangulation of closed polygons [triangulationofclosedpolygons]
|
|
|
|
The generation of high-quality triangle and tetrahedral meshes is a very common
|
|
task in geometry processing. We provide wrappers in libigl to
|
|
[triangle](http://www.cs.cmu.edu/~quake/triangle.html) and
|
|
[Tetgen](http://wias-berlin.de/software/tetgen/).
|
|
|
|
A triangle mesh with a given boundary can be created with:
|
|
|
|
```cpp
|
|
igl::triangulate(V,E,H,V2,F2,"a0.005q");
|
|
```
|
|
|
|
where `E` is a set of boundary edges (#E by 2), `H` is a set of 2D positions of
|
|
points contained in holes of the triangulation (#H by 2) and (`V2`,`F2`) is the
|
|
generated triangulation. Additional parameters can be passed to `triangle`, to
|
|
control the quality: `"a0.005q"` enforces a bound on the maximal area of the
|
|
triangles and a minimal angle of 20 degrees. In [Example
|
|
604](604_Triangle/main.m), the interior of a square (excluded a smaller square
|
|
in its interior) is triangulated.
|
|
|
|
|
|
|
|
## Tetrahedralization of closed surfaces [tetrahedralizationofclosedsurfaces]
|
|
|
|
Similarly, the interior of a closed manifold surface can be tetrahedralized
|
|
using the function `igl::tetrahedralize` which wraps the Tetgen library ([Example
|
|
605](605_Tetgen/main.c)):
|
|
|
|
```cpp
|
|
igl::tetrahedralize(V,F,"pq1.414", TV,TT,TF);
|
|
```
|
|
|
|
|
|
|
|
## Baking ambient occlusion [bakingambientocclusion]
|
|
|
|
[Ambient occlusion](http://en.wikipedia.org/wiki/Ambient_occlusion) is a
|
|
rendering technique used to calculate the exposure of each point in a surface
|
|
to ambient lighting. It is usually encoded as a scalar (normalized between 0
|
|
and 1) associated with the vertice of a mesh.
|
|
|
|
Formally, ambient occlusion is defined as:
|
|
|
|
\\[ A_p = \frac{1}{\pi} \int_\omega V_{p,\omega}(n \cdot \omega) d\omega \\]
|
|
|
|
where $V_{p,\omega}$ is the visibility function at p, defined to be zero if p
|
|
is occluded in the direction $\omega$ and one otherwise, and $d\omega$ is the
|
|
infinitesimal solid angle step of the integration variable $\omega$.
|
|
|
|
The integral is usually approximated by casting rays in random directions
|
|
around each vertex. This approximation can be computed using the function:
|
|
|
|
```cpp
|
|
igl::ambient_occlusion(V,F,V_samples,N_samples,500,AO);
|
|
```
|
|
|
|
that given a scene described in `V` and `F`, computes the ambient occlusion of
|
|
the points in `V_samples` whose associated normals are `N_samples`. The
|
|
number of casted rays can be controlled (usually at least 300-500 rays are
|
|
required to get a smooth result) and the result is returned in `AO`, as a
|
|
single scalar for each sample.
|
|
|
|
Ambient occlusion can be used to darken the surface colors, as shown in
|
|
[Example 606](606_AmbientOcclusion/main.c)
|
|
|
|
|
|
|
|
## Picking [pickingverticesandfaces]
|
|
|
|
Picking vertices and faces using the mouse is very common in geometry
|
|
processing applications. While this might seem a simple operation, its
|
|
implementation is not straighforward. Libigl contains a function that solves this problem using the
|
|
[Embree](https://software.intel.com/en-us/articles/embree-photo-realistic-ray-tracing-kernels)
|
|
raycaster. Its usage is demonstrated in [Example 607](607_Picking/main.cpp):
|
|
|
|
```cpp
|
|
bool hit = igl::unproject_onto_mesh(
|
|
Vector2f(x,y),
|
|
F,
|
|
viewer.core.view * viewer.core.model,
|
|
viewer.core.proj,
|
|
viewer.core.viewport,
|
|
*ei,
|
|
fid,
|
|
vid);
|
|
```
|
|
|
|
This function casts a ray from the view plane in the view direction. Variables
|
|
`x` and `y` are
|
|
the mouse screen coordinates; `view`, `model`, `proj` are the view, model and
|
|
projection matrix respectively; `viewport` is the viewport in OpenGL format;
|
|
`ei`
|
|
contains a [Bounding Volume
|
|
Hierarchy](http://en.wikipedia.org/wiki/Bounding_volume_hierarchy) constructed
|
|
by Embree, and `fid` and `vid` are the picked face and vertex, respectively.
|
|
|
|
) Picking via ray casting. The selected
|
|
vertices are colored in red.](images/607_Picking.png)
|
|
|
|
## Locally Injective Maps [locallyinjectivemaps]
|
|
|
|
Extreme deformations or parametrizations with high-distortion might flip
|
|
elements. This is undesirable in many applications, and it is possible to
|
|
avoid it by introducing a non-linear constraints that guarantees that the area
|
|
of every element remain positive.
|
|
|
|
Libigl can be used to compute Locally Injective Maps [#schuller_2013][] using a variety of
|
|
deformation energies. A simple deformation of a 2D grid is computed in [Example
|
|
608](608_LIM/main.cpp).
|
|
|
|
|
|
|
|
## Boolean operations on meshes [booleanoperationsonmeshes]
|
|
|
|
Constructive solid geometry (CSG) is a technique to define a complex surface as
|
|
the result of a number of set operations on solid regions of space: union,
|
|
intersection, set difference, symmetric difference, complement. Typically, CSG
|
|
libraries represent the inputs and outputs to these operations _implicitly_:
|
|
the solid $A$ is defined as the open set of points $\mathbf{x}$ for which some
|
|
function $a(\mathbf{x})$ "returns true". The surface of this shape is the
|
|
_closure_ of all points $x$ in $A$.
|
|
|
|
With this sort of representation, boolean
|
|
operations are straightforward. For example, the union of solids $A$ and $B$
|
|
is simply
|
|
|
|
$A \cup B = \{\mathbf{x} \left.\right|
|
|
a(\mathbf{x}) \text{ or } b(\mathbf{x})\},$
|
|
|
|
the intersection is
|
|
|
|
$A \cap B = \{\mathbf{x} \left.\right|
|
|
a(\mathbf{x}) \text{ and } b(\mathbf{x})\},$
|
|
|
|
the difference $A$ _minus_ $B$ is
|
|
|
|
$A \setminus B = \{\mathbf{x} \left.\right|
|
|
a(\mathbf{x}) \text{ and _not_ } b(\mathbf{x})\},$
|
|
|
|
and the symmetric difference (XOR) is
|
|
|
|
$A \triangle B = \{\mathbf{x} \left.\right|
|
|
\text{either } a(\mathbf{x}) \text{ or } b(\mathbf{x}) \text{ but not both }\}.$
|
|
|
|
Stringing together many of these operations, one can design quite complex
|
|
shapes. A typical CSG library might only keep explicit _base-case_
|
|
representations of canonical shapes: half-spaces, quadrics, etc.
|
|
|
|
In libigl, we do currently _not_ have an implicit surface representation.
|
|
Instead we expect our users to be working with _explicit_ triangle mesh
|
|
_boundary representations_ of solid shapes. CSG operations are much hard to
|
|
compute robustly with boundary representations, but are nonetheless useful.
|
|
|
|
To compute a boolean operation on a triangle mesh with vertices `VA` and
|
|
triangles `FA` and another mesh `VB` and `FB`, libigl first computes a unified
|
|
mesh with vertices `V` and triangles `F` where all triangle-triangle
|
|
intersections have been "resolved". That is, edges and vertices are added
|
|
exactly at the intersection lines, so the resulting _non-manifold_ mesh `(V,F)`
|
|
has no self-intersections.
|
|
|
|
Then libigl _peels_ the outer hull [#attene_2014][] off this mesh recursively,
|
|
keeping track of the iteration parity and orientation flips for each layer.
|
|
For any boolean operation, these two pieces of information determine for each
|
|
triangle (1) if it should be included in the output, and (2) if its orientation
|
|
should be reversed before added to the output.
|
|
|
|
Calling libigl's boolean operations is simple. To compute the union of
|
|
`(VA,FA)` and `(VB,FB)` into a new mesh `(VC,FC)`, use:
|
|
|
|
```cpp
|
|
igl::mesh_boolean(VA,FA,VB,FB,MESH_BOOLEAN_TYPE_UNION,VC,FC);
|
|
```
|
|
|
|
The following figure shows each boolean operation on two meshes.
|
|
|
|
 conducts
|
|
boolean operations on the _Cheburashka_ (red) and _Knight_ (green). From left
|
|
to right: union, intersection, set minus, symmetric difference (XOR),
|
|
"resolve". Bottom row reveals inner surfaces, darker color indicates
|
|
back-facing triangles.](images/cheburashka-knight-boolean.jpg)
|
|
|
|
The union, symmetric difference and "resolve" have the same outward
|
|
appearance, but differ in their treatment of internal structures. The union has
|
|
no internal surfaces: the triangles are not included in the output. The
|
|
symmetric difference is the same set of triangles as the "resolve", but
|
|
internal surfaces have been reversed in orientation, indicating that the solid
|
|
result of the operation. The "resolve" operation is not really a boolean
|
|
operation, it is simply the result of resolving all intersections and gluing
|
|
together coincident vertices, maintaining original triangle orientations.
|
|
|
|
Libigl also provides a wrapper `igl::mesh_boolean_cork` to the
|
|
[cork](https://github.com/gilbo/cork), which is typically faster, but is not
|
|
always robust.
|
|
|
|
# Miscellaneous [chapter7:miscellaneous]
|
|
|
|
Libigl contains a _wide_ variety of geometry processing tools and functions for
|
|
dealing with meshes and the linear algebra related to them: far too many to
|
|
discuss in this introductory tutorial. We've pulled out a couple of the
|
|
interesting functions in this chapter to highlight.
|
|
|
|
## Mesh Statistics [meshstatistics]
|
|
|
|
Libigl contains various mesh statistics, including face angles, face areas and
|
|
the detection of singular vertices, which are vertices with more or less than 6
|
|
neighbours in triangulations or 4 in quadrangulations.
|
|
|
|
The example [Statistics](701_Statistics/main.cpp) computes these quantities and
|
|
does a basic statistic analysis that allows to estimate the isometry and
|
|
regularity of a mesh:
|
|
|
|
```bash
|
|
Irregular vertices:
|
|
136/2400 (5.67%)
|
|
Areas (Min/Max)/Avg_Area Sigma:
|
|
0.01/5.33 (0.87)
|
|
Angles in degrees (Min/Max) Sigma:
|
|
17.21/171.79 (15.36)
|
|
```
|
|
|
|
The first row contains the number and percentage of irregular vertices, which
|
|
is particularly important for quadrilateral meshes when they are used to define
|
|
subdivision surfaces: every singular point will result in a point of the
|
|
surface that is only C^1.
|
|
|
|
The second row reports the area of the minimal element, maximal element and the
|
|
standard deviation. These numbers are normalized by the mean area, so in the
|
|
example above 5.33 max area means that the biggest face is 5 times larger than
|
|
the average face. An ideal isotropic mesh would have both min and max area
|
|
close to 1.
|
|
|
|
The third row measures the face angles, which should be close to 60 degrees (90
|
|
for quads) in a perfectly regular triangulation. For FEM purposes, the closer
|
|
the angles are to 60 degrees the more stable will the optimization be. In this
|
|
case, it is clear that the mesh is of bad quality and it will probably result
|
|
in artifacts if used for solving PDEs.
|
|
|
|
## Generalized Winding Number [generalizedwindingnumber]
|
|
|
|
The problem of tetrahedralizing the interior of closed watertight surface mesh
|
|
is a difficult, but well-posed problem (see our [Tetgen wrappers][tetrahedralizationofclosedsurfaces]). But
|
|
black-box tet-meshers like TetGen will _refuse_ input triangle meshes with
|
|
self-intersections, open boundaries, non-manifold edges from multiple connected
|
|
components.
|
|
The problem is two-fold: self-intersections present contradictory facet
|
|
constraints and self-intersections/open-boundaries/non-manifold edges make the
|
|
problem of determining inside from outside ill-posed without further
|
|
assumptions.
|
|
|
|
The first problem is _easily_ solved by "resolving" all self-intersections.
|
|
That is, meshing intersecting triangles so that intersects occur exactly at
|
|
edges and vertices. This is accomplished using `igl::selfintersect`.
|
|
|
|
TetGen can usually tetrahedralize the convex hull of this "resolved" mesh, and
|
|
then the problem becomes determining which of these tets are _inside_ the input
|
|
mesh and which are outside. That is, which should be kept and which should be
|
|
removed.
|
|
|
|
The "Generalized Winding Number" is a robust method for determined
|
|
inside and outside for troublesome meshes [#jacobson_2013][]. The generalized
|
|
winding number with respect to `(V,F)` at some point $\mathbf{p} \in
|
|
\mathcal{R}^3$ is defined as scalar function:
|
|
|
|
$$w(\mathbf{p}) = \sum\limits_{f_i\in F} \frac{1}{4\pi}\Omega_{f_i}(\mathbf{p})$$
|
|
|
|
where $\Omega_{f_i}$ is the _solid angle_ subtended by $f_i$ (the ith face in
|
|
`F`) at the point $\mathbf{p}$. This solid angle contribution is a simple,
|
|
closed-form expression involving `atan2` and some dot-products.
|
|
|
|
If `(V,F)` _does_ form a closed watertight surface, then $w(\mathbf{p})=1$ if
|
|
$\mathbf{p}$ lies inside `(V,F)` and $w(\mathbf{p})=0$ if outside `(V,F)`. If
|
|
`(V,F)` is closed but overlaps itself then $w(\mathbf{p})$ is an integer value
|
|
counting how many (signed) times `(V,F)` _wraps_ around $\mathbf{p}$. Finally,
|
|
if `(V,F)` is not closed or not even manifold (but at least consistently
|
|
oriented), then $w(\mathbf{p})$ tends smoothly toward 1 as $\mathbf{p}$ is
|
|
_more_ inside `(V,F)`, and toward 0 as $\mathbf{p}$ is more outside.
|
|
|
|
 computes the
|
|
generalized winding number function for a tetrahedral mesh inside a cat with
|
|
holes and self intersections (gold). The silver mesh is surface of the
|
|
extracted interior tets, and slices show the winding number function on all
|
|
tets in the convex hull: blue (~0), green (~1), yellow
|
|
(~2).](images/big-sigcat-winding-number.gif)
|
|
|
|
## Mesh Decimation [meshdecimation]
|
|
|
|
The study of mesh simplification or _decimation_ is nearly as old as meshes
|
|
themselves. Given a high resolution mesh with too many triangles, find a "well
|
|
approximating" low resolution mesh with far fewer triangles. By now there are a
|
|
variety of different paradigms for solving this problem and state-of-the-art
|
|
methods are fairly advanced.
|
|
|
|
One family of mesh decimation methods operates by successively remove elements
|
|
from the mesh. In particular, Hoppe advocates for successively remove or rather
|
|
collapsing edges [#hoppe_1996][]. The generic form of this technique is to
|
|
construct a sequence of n meshes from the initial high-resolution mesh $M_0$ to
|
|
the lowest resolution mesh $M_n$ by collapsing a single edge:
|
|
|
|
$M_0 \mathop{\longrightarrow}_\text{edge collapse}
|
|
M_1 \mathop{\longrightarrow}_\text{edge collapse}
|
|
\dots \mathop{\longrightarrow}_\text{edge collapse}
|
|
M_{n-1} \mathop{\longrightarrow}_\text{edge collapse} M_n.$
|
|
|
|
Hoppe's original method and subsequent follow-up works propose various ways to
|
|
choose the next edge to collapse in this sequence. Using a cost-based paradigm,
|
|
one can maintain a priority queue of edges based on their "cost" (how much
|
|
"worse" will my approximation be if I remove this edge?). The cheapest edge is
|
|
collapsed and costs of neighboring edges are updated.
|
|
|
|
In order to maintain the topology (e.g. if the mesh is combinatorially as
|
|
sphere or a torus etc.), one should assign infinite cost to edges whose
|
|
collapse would alter the mesh topology. Indeed this happens if and only if the
|
|
number of mutual neighbors of the endpoints of the collapsing edge is not
|
|
exactly two!
|
|
|
|
If there exists a third shared vertex, then another face will be removed, but 2
|
|
edges will be removed. This can result in unwanted holes or non-manifold
|
|
"flaps".
|
|
|
|
|
|
|
|
> There is also a one-off condition that no edges of a tetrahedron should be
|
|
> collapsed.
|
|
|
|
Because libigl (purposefully) does not center its implementations around a
|
|
dynamic mesh data structure (e.g. half-edge datastructure), support for
|
|
topology changes are limited. Nonetheless, libigl has support for isolated edge
|
|
collapses, sequences of edge-collapses (each in O(log) time) and priority queue
|
|
based decimation.
|
|
|
|
The simplest is `igl::decimation`. By calling
|
|
|
|
```cpp
|
|
igl::decimate(V,F,1000,U,G);
|
|
```
|
|
|
|
the mesh `(V,F)` will be decimated to a new mesh `(U,G)` so that `G` has at
|
|
most `1000` faces. This uses default (naive) criteria for determining the cost
|
|
of an edge collapse and the placement of the merged vertex. Shortest edges are
|
|
collapsed first, and merged vertices are placed at edge midpoints.
|
|
|
|
One can also provide function handles (`c++` lambda functions are convenient
|
|
here) `cost_and_placement` and `stopping_condition` for determining the
|
|
cost/placement of an edge collapse and the stopping condition respectively. For
|
|
example, the default version above is implemented as:
|
|
|
|
```cpp
|
|
igl::decimate(V,F,shortest_edge_and_midpoint,max_m,U,G);
|
|
```
|
|
|
|
where `shortest_edge_and_midpoint` assign the edge's length as cost and its
|
|
midpoint as the merged vertex placement and `max_m` counts the current number
|
|
of faces (valid collapses decrease count by 2) and returns `true` if the count
|
|
drops below `m=1000`.
|
|
|
|
One can also scratch deeper inside the decimation loop and call
|
|
`igl::collapse_edge` directly. In order to operate efficiently, this routine
|
|
needs more than the usual `(V,F)` mesh representation. We need `E` a list of
|
|
edge indices, where `E.row(i) --> [s,d]`; we need `EMAP` which maps the
|
|
"half"-edges of each triangle in `F` to its corresponding edge in `E` so that
|
|
`E.row(EMAP(f+i*F.rows)) --> [s,d]` if the edge across from the ith corner of the
|
|
fth face is `[s,d]` (up to orientation); we need `EF` and `EI` which keep track
|
|
of the faces incident on each edge and across from which corner of those faces
|
|
the edges appears, so that `EF(e,o) = f` and `EI(e,o) = i` means that the edge
|
|
`E.row(e) --> [s,d]` appears in the fth face across from its ith corner (for
|
|
`o=0` the edge orientations should match, for `o=1` the orientations are
|
|
opposite).
|
|
|
|
When a collapse occurs, the sizes of the `F`,`E`, etc. matrices do not change.
|
|
Rather rows corresponding to "removed" faces and edges are set to a special
|
|
constant value `IGL_COLLAPSE_EDGE_NULL`. Doing this ensures that we're able to
|
|
remove edges in truly constant time O(1).
|
|
|
|
|
|
> Conveniently `IGL_COLLAPSE_EDGE_NULL==0`. This means most OPENGL style renderings of `F`
|
|
> will simply draw a bunch of 0-area triangles at the first vertex.
|
|
|
|
The following will collapse the first
|
|
edge and place its merged vertex at the origin:
|
|
|
|
```cpp
|
|
igl::collapse_edge(0,RowVector3d(0,0,0),V,F,E,EMAP,EF,EI);
|
|
```
|
|
If valid, then `V`,`F`,`E`,`EF`,`EI` are adjusted accordingly.
|
|
|
|
This is powerful, but low level. To build a decimator around this you'd need to
|
|
keep track which edges are left to collapse and which to collapse next.
|
|
Fortunately, libigl also exposes a priority queue based edge collapse with
|
|
function handles to adjust costs and placements.
|
|
|
|
The priority queue is implemented as a (ordered) set `Q` or (cost,edge index)
|
|
pairs and a list of iterators `Qit` so that `Qit[e]` reveals the iterator in
|
|
`Q` corresponding to the eth edge. Placements are stored in a #E list of
|
|
positions `C`. When the following is called:
|
|
|
|
```cpp
|
|
igl::collapse_edge(cost_and_placement,V,F,E,EMAP,EF,EI,Q,Qit,C);
|
|
```
|
|
|
|
the lowest cost edge collapse according to `Q` is attempted. If valid, then
|
|
`V`,`F`,etc. are adjusted accordingly and that edge is "popped" from `Q`. Using
|
|
`Qit` its neighboring edges are also popped from `Q` and re-inserted after
|
|
updating their costs according to `cost_and_placement`, new placements are
|
|
remembered in `C`. If not valid, then the edge is "popped" from `Q` and
|
|
reinserted with infinite cost.
|
|
|
|
|
|
|
|
The [Example 703](./703_Decimation/main.cpp) demonstrates using this priority
|
|
queue based approach with the simple shortest-edge-midpoint cost/placement
|
|
strategy discussed above.
|
|
|
|
## Signed Distances [signeddistances]
|
|
|
|
In the [Generalized Winding Number section][generalizedwindingnumber], we
|
|
examined a robust method for determining whether points lie inside or outside
|
|
of a given triangle soup mesh. Libigl complements this algorithm with
|
|
accelerated signed and unsigned distance queries and "in element" queries for
|
|
planar triangle meshes and 3D tetrahedral meshes. These routines make use of
|
|
libigl's general purpose axis-aligned bounding box hierarchy (`igl/AABB.h`).
|
|
This class is lightweight and---by design---does not store a copy of the mesh
|
|
(taking it as inputs to its member functions instead).
|
|
|
|
### Point location
|
|
For tetrahedral meshes, this is useful for "in element" or "point location"
|
|
queries: given a point $\mathbf{q}\in\mathcal{R}^3$ and a tetrahedral mesh
|
|
$(V,T)$ determine in which tetrahedron $\mathbf{q}$ lies. This is accomplished
|
|
in libigl for a tet mesh `V,T` and a list of query points in the rows of `Q`
|
|
via the `igl::in_element()`:
|
|
|
|
```cpp
|
|
// Initialize AABB tree
|
|
igl::AABB<MatrixXd,3> tree;
|
|
tree.init(V,T);
|
|
VectorXi I;
|
|
igl::in_element(V,T,Q,tree,I);
|
|
```
|
|
|
|
the resulting vector `I` is a list of indices into `T` revealing the _first_
|
|
tetrahedron found to contain the corresponding point in `Q`.
|
|
|
|
For overlapping meshes, a point $\mathbf{q}$ may belong to more than one
|
|
tetrahedron. In those cases, one can find them all (not just the first) by
|
|
using the `igl::in_element` overload with a `SparseMatrix` as the output:
|
|
|
|
```cpp
|
|
SparseMatrix<int> I;
|
|
igl::in_element(V,T,Q,tree,I);
|
|
```
|
|
|
|
now each row of `I` reveals whether each tet contains the corresponding row in
|
|
`Q`: `I(q,e)!=0` means that point `q` is in element `e`.
|
|
|
|
### Closest points
|
|
|
|
For Triangle meshes, we use the AABB tree to accelerate point-mesh closest
|
|
point queries: given a mesh $(V,F)$ and a query point
|
|
$\mathbf{q}\in\mathcal{R}^3$ find the closest point $\mathbf{c} \in (V,F)$
|
|
(where $\mathbf{c}$ is not necessarily a vertex of $(V,F)$). This is
|
|
accomplished for a triangle mesh `V,F` and a list of points in the rows of `P`
|
|
via `igl::point_mesh_squared_distance`:
|
|
|
|
```cpp
|
|
VectorXd sqrD;
|
|
VectorXi I;
|
|
MatrixXd C;
|
|
igl::point_mesh_squared_distance(P,V,F,sqrD,I,C);
|
|
```
|
|
|
|
the output `sqrD` contains the (unsigned) squared distance from each point in
|
|
`P` to its closest point given in `C` which lies on the element in `F` given by
|
|
`I` (e.g. from which one could recover barycentric coordinates, using
|
|
`igl::barycentric_coordinates`).
|
|
|
|
If the mesh `V,F` is static, but the point set `P` is changing dynamically then
|
|
it's best to reuse the AABB hierarchy that's being built during
|
|
`igl::point_mesh_squared_distance`:
|
|
|
|
```cpp
|
|
igl::AABB tree;
|
|
tree.init(V,F);
|
|
tree.squared_distance(V,F,P,sqrD,I,C);
|
|
... // P changes, but (V,F) does not
|
|
tree.squared_distance(V,F,P,sqrD,I,C);
|
|
```
|
|
|
|
### Signed distance
|
|
|
|
Finally, from the closest point or the winding number it's possible to _sign_
|
|
this distance. In `igl::signed_distance` we provide two methods for signing:
|
|
the so-called "pseudo-normal test" [#baerentzen_2005][] and the generalized
|
|
winding number [#jacobson_2013][].
|
|
|
|
The pseudo-normal test (see also `igl::pseudonormal_test`) assumes the input
|
|
mesh is a watertight (closed, non-self-intersecting, manifold) mesh. Then given
|
|
a query point $\mathbf{q}$ and its closest point $\mathbf{c} \in (V,F)$, it
|
|
carefully chooses an outward normal $\mathbf{n}$ at $\mathbf{c}$ so that
|
|
$\text{sign}(\mathbf{q}-\mathbf{c})\cdot \mathbf{n}$ reveals whether
|
|
$\mathbf{q}$ is inside $(V,F)$: -1, or outside: +1. This is a fast $O(1)$ test
|
|
once $\mathbf{c}$ is located, but may fail if `V,F` is not watertight.
|
|
|
|
An alternative is to use the [generalized winding
|
|
number][generalizedwindingnumber] to determine the sign. This is very robust to
|
|
unclean meshes `V,F` but slower: something like $O(\sqrt{n})$ once $\mathbf{c}$
|
|
is located.
|
|
|
|
In either case, the interface via `igl::signed_distance` is:
|
|
|
|
```cpp
|
|
// Choose type of signing to use
|
|
igl::SignedDistanceType type = SIGNED_DISTANCE_TYPE_PSEUDONORMAL;
|
|
igl::signed_distance(P,V,F,sign_type,S,I,C,N);
|
|
```
|
|
|
|
the outputs are as above for `igl::point_mesh_squared_distance` but now `S`
|
|
contains signed (unsquared) distances and the extra output `N` (only set when
|
|
`type == SIGNED_DISTANCE_TYPE_PSEUDON`) contains the normals used for signing
|
|
with the pseudo-normal test.
|
|
|
|
 computes signed distance on
|
|
slices through the bunny.](images/bunny-signed-distance.gif)
|
|
|
|
|
|
# Outlook for continuing development [future]
|
|
|
|
Libigl is in active development, and we plan to focus on the following features
|
|
in the next months:
|
|
|
|
* A better and more consistent **documentation**, plus extending this tutorial
|
|
to cover more libigl features.
|
|
|
|
* Implement a **mixed-integer solver** which only uses Eigen to remove the
|
|
dependency on CoMiSo.
|
|
|
|
* Improve the robustness and performance of the active set QP solver. In
|
|
particular, handle linearly dependent constraints.
|
|
|
|
* Implement more mesh analysis functions, including structural analysis for
|
|
masonry and _3D-printability_ analysis.
|
|
|
|
* Increase support for point clouds and general polygonal meshes.
|
|
|
|
* What would you like to see in libigl? [Contact
|
|
us!](mailto:alecjacobson@gmail.com) or post a [feature
|
|
request](https://github.com/libigl/libigl/issues/new).
|
|
|
|
We encourage you to contribute to the library and to report problems and bugs.
|
|
The best way to contribute new feature or bug fixes is to fork the libigl
|
|
repository and to open a [pull
|
|
request](https://help.github.com/articles/using-pull-requests) on [our github
|
|
repository](https://github.com/libigl/libigl).
|
|
|
|
[#attene_2014]: Marco Attene.
|
|
[Direct repair of self-intersecting
|
|
meshes](https://www.google.com/search?q=Direct+repair+of+self-intersecting+meshes),
|
|
2014.
|
|
[#baerentzen_2005]: J Andreas Baerentzen and Henrik Aanaes.
|
|
[Signed distance computation using the angle weighted
|
|
pseudonormal](https://www.google.com/search?q=Signed+distance+computation+using+the+angle+weighted+pseudonormal),
|
|
2005.
|
|
[#barbic_2005]: Jernej Barbic and Doug James. [Real-Time Subspace Integration
|
|
for St.Venant-Kirchhoff Deformable
|
|
Models](https://www.google.com/search?q=Real-Time+Subspace+Integration+for+St.Venant-Kirchhoff+Deformable+Models),
|
|
2005.
|
|
[#bommes_2009]: David Bommes, Henrik Zimmer, Leif Kobbelt.
|
|
[Mixed-integer
|
|
quadrangulation](http://www-sop.inria.fr/members/David.Bommes/publications/miq.pdf),
|
|
2009.
|
|
[#botsch_2004]: Matrio Botsch and Leif Kobbelt.
|
|
[An Intuitive Framework for Real-Time Freeform
|
|
Modeling](https://www.google.com/search?q=An+Intuitive+Framework+for+Real-Time+Freeform+Modeling),
|
|
2004.
|
|
[#bouaziz_2012]: Sofien Bouaziz, Mario Deuss, Yuliy Schwartzburg, Thibaut Weise, Mark Pauly
|
|
[Shape-Up: Shaping Discrete Geometry with
|
|
Projections](http://lgg.epfl.ch/publications/2012/shapeup.pdf), 2012
|
|
[#chao_2010]: Isaac Chao, Ulrich Pinkall, Patrick Sanan, Peter Schröder.
|
|
[A Simple Geometric Model for Elastic
|
|
Deformations](https://www.google.com/search?q=A+Simple+Geometric+Model+for+Elastic+Deformations),
|
|
2010.
|
|
[#diamanti_2014]: Olga Diamanti, Amir Vaxman, Daniele Panozzo, Olga
|
|
Sorkine-Hornung. [Designing N-PolyVector Fields with Complex
|
|
Polynomials](http://igl.ethz.ch/projects/complex-roots/), 2014
|
|
[#eck_2005]: Matthias Eck, Tony DeRose, Tom Duchamp, Hugues Hoppe, Michael Lounsbery, Werner
|
|
Stuetzle. [Multiresolution Analysis of Arbitrary
|
|
Meshes](http://research.microsoft.com/en-us/um/people/hoppe/mra.pdf), 2005.
|
|
[#hildebrandt_2011]: Klaus Hildebrandt, Christian Schulz, Christoph von
|
|
Tycowicz, and Konrad Polthier. [Interactive Surface Modeling using Modal
|
|
Analysis](https://www.google.com/search?q=Interactive+Surface+Modeling+using+Modal+Analysis),
|
|
2011.
|
|
[#hoppe_1996]: Hugues Hoppe. [Progressive
|
|
Meshes](https://www.google.com/search?q=Progressive+meshes), 1996
|
|
[#jacobson_skinning_course_2014]: Alec Jacobson, Zhigang Deng, Ladislav Kavan,
|
|
J.P. Lewis. [_Skinning: Real-Time Shape
|
|
Deformation_](https://www.google.com/search?q=Skinning+Real-Time+Shape+Deformation),
|
|
2014.
|
|
[#jacobson_thesis_2013]: Alec Jacobson,
|
|
[_Algorithms and Interfaces for Real-Time Deformation of 2D and 3D
|
|
Shapes_](https://www.google.com/search?q=Algorithms+and+Interfaces+for+Real-Time+Deformation+of+2D+and+3D+Shapes),
|
|
2013.
|
|
[#jacobson_2013]: Alec Jacobson, Ladislav Kavan, and Olga Sorkine.
|
|
[Robust Inside-Outside Segmentation using Generalized Winding
|
|
Numbers](https://www.google.com/search?q=Robust+Inside-Outside+Segmentation+using+Generalized+Winding+Numbers),
|
|
2013.
|
|
[#jacobson_2012]: Alec Jacobson, Ilya Baran, Ladislav Kavan, Jovan Popović, and
|
|
Olga Sorkine. [Fast Automatic Skinning
|
|
Transformations](https://www.google.com/search?q=Fast+Automatic+Skinning+Transformations),
|
|
2012.
|
|
[#jacobson_2011]: Alec Jacobson, Ilya Baran, Jovan Popović, and Olga Sorkine.
|
|
[Bounded Biharmonic Weights for Real-Time
|
|
Deformation](https://www.google.com/search?q=Bounded+biharmonic+weights+for+real-time+deformation),
|
|
2011.
|
|
[#jacobson_mixed_2010]: Alec Jacobson, Elif Tosun, Olga Sorkine, and Denis
|
|
Zorin. [Mixed Finite Elements for Variational Surface
|
|
Modeling](https://www.google.com/search?q=Mixed+Finite+Elements+for+Variational+Surface+Modeling),
|
|
2010.
|
|
[#kavan_2008]: Ladislav Kavan, Steven Collins, Jiri Zara, and Carol O'Sullivan.
|
|
[Geometric Skinning with Approximate Dual Quaternion
|
|
Blending](https://www.google.com/search?q=Geometric+Skinning+with+Approximate+Dual+Quaternion+Blending),
|
|
2008.
|
|
[#kazhdan_2012]: Michael Kazhdan, Jake Solomon, Mirela Ben-Chen,
|
|
[Can Mean-Curvature Flow Be Made
|
|
Non-Singular](https://www.google.com/search?q=Can+Mean-Curvature+Flow+Be+Made+Non-Singular),
|
|
2012.
|
|
[#knoppel_2013]: Felix Knöppel, Keenan Crane, Ulrich Pinkall, and Peter
|
|
Schröder. [Globally Optimal Direction
|
|
Fields](http://www.cs.columbia.edu/~keenan/Projects/GloballyOptimalDirectionFields/paper.pdf),
|
|
2013.
|
|
[#levy_2002]: Bruno Lévy, Sylvain Petitjean, Nicolas Ray, Jérome Maillot.
|
|
[Least Squares Conformal Maps, for Automatic Texture Atlas
|
|
Generation,](http://www.cs.jhu.edu/~misha/Fall09/Levy02.pdf), 2002.
|
|
[#levy_2008]: Nicolas Ray, Bruno Vallet, Wan Chiu Li, Bruno Lévy.
|
|
[N-Symmetry Direction Field
|
|
Design](http://alice.loria.fr/publications/papers/2008/DGF/NSDFD-TOG.pdf),
|
|
2008.
|
|
[#liu_2008]: Ligang Liu, Lei Zhang, Yin Xu, Craig Gotsman, Steven J. Gortler.
|
|
[A Local/Global Approach to Mesh
|
|
Parameterization](http://cs.harvard.edu/~sjg/papers/arap.pdf), 2008.
|
|
[#liu_2011]: Yang Liu, Weiwei Xu, Jun Wang, Lifeng Zhu, Baining Guo, Falai Chen, Guoping
|
|
Wang. [General Planar Quadrilateral Mesh Design Using Conjugate Direction
|
|
Field](http://research.microsoft.com/en-us/um/people/yangliu/publication/cdf.pdf),
|
|
2008.
|
|
[#mcadams_2011]: Alexa McAdams, Andrew Selle, Rasmus Tamstorf, Joseph Teran,
|
|
Eftychios Sifakis. [Computing the Singular Value Decomposition of 3x3
|
|
matrices with minimal branching and elementary floating point
|
|
operations](https://www.google.com/search?q=Computing+the+Singular+Value+Decomposition+of+3x3+matrices+with+minimal+branching+and+elementary+floating+point+operations),
|
|
2011.
|
|
[#meyer_2003]: Mark Meyer, Mathieu Desbrun, Peter Schröder and Alan H. Barr,
|
|
[Discrete Differential-Geometry Operators for Triangulated
|
|
2-Manifolds](https://www.google.com/search?q=Discrete+Differential-Geometry+Operators+for+Triangulated+2-Manifolds),
|
|
2003.
|
|
[#mullen_2008]: Patrick Mullen, Yiying Tong, Pierre Alliez, Mathieu Desbrun.
|
|
[Spectral Conformal
|
|
Parameterization](http://www.geometry.caltech.edu/pubs/MTAD08.pdf), 2008.
|
|
[#panozzo_2010]: Daniele Panozzo, Enrico Puppo, Luigi Rocca, [Efficient
|
|
Multi-scale Curvature and Crease
|
|
Estimation](https://www.google.com/search?q=Efficient+Multi-scale+Curvature+and+Crease+Estimation),
|
|
2010.
|
|
[#panozzo_2014]: Daniele Panozzo, Enrico Puppo, Marco Tarini, Olga
|
|
Sorkine-Hornung. [Frame Fields: Anisotropic and Non-Orthogonal Cross
|
|
Fields](http://www.inf.ethz.ch/personal/dpanozzo/papers/frame-fields-2014.pdf),
|
|
2014.
|
|
[#rustamov_2011]: Raid M. Rustamov, [Multiscale Biharmonic
|
|
Kernels](https://www.google.com/search?q=Multiscale+Biharmonic+Kernels), 2011.
|
|
[#schuller_2013]: Christian Schüller, Ladislav Kavan, Daniele Panozzo, Olga
|
|
Sorkine-Hornung. [Locally Injective
|
|
Mappings](http://igl.ethz.ch/projects/LIM/), 2013.
|
|
[#sharf_2007]: Andrei Sharf, Thomas Lewiner, Gil Shklarski, Sivan Toledo, and
|
|
Daniel Cohen-Or. [Interactive topology-aware surface
|
|
reconstruction](https://www.google.com/search?q=Interactive+topology-aware+surface+reconstruction),
|
|
2007.
|
|
[#sorkine_2004]: Olga Sorkine, Yaron Lipman, Daniel Cohen-Or, Marc Alexa,
|
|
Christian Rössl and Hans-Peter Seidel. [Laplacian Surface
|
|
Editing](https://www.google.com/search?q=Laplacian+Surface+Editing), 2004.
|
|
[#sorkine_2007]: Olga Sorkine and Marc Alexa, [As-rigid-as-possible Surface
|
|
Modeling](https://www.google.com/search?q=As-rigid-as-possible+Surface+Modeling), 2007.
|
|
[#vallet_2008]: Bruno Vallet and Bruno Lévy. [Spectral Geometry Processing with
|
|
Manifold
|
|
Harmonics](https://www.google.com/search?q=Spectral+Geometry+Processing+with+Manifold+Harmonics),
|
|
2008.
|
|
[#wang_bc_2015]: Yu Wang, Alec Jacobson, Jernej Barbic, Ladislav Kavan. [Linear
|
|
Subspace Design for Real-Time Shape
|
|
Deformation](https://www.google.com/search?q=Linear+Subspace+Design+for+Real-Time+Shape+Deformation),
|
|
2015
|