This package contains the code that implements the following paper,

Renjie Chen and Ofir Weber. GPU-Accelerated Locally Injective Shape Deformation. ACM Transactions on Graphics, 36(6) (SIGGRAPH Asia 2017)

What does the code contain.

The app is built with a combination of MATLAB (for core computation), C++ code (for the UI) and mex/CUDA code (for GPU accelerated optimization). The C++ source code for the OpenGL UI with MS Visual Studio C++ project files is in the glidviewer folder.
The mex/CUDA source code for the GPU accelerated optimization is in the cuHarmonic folder. Precompiled binary for the UI and mex/CUDA are provided with the package.

Requirements:

• MS Windows (Windows 7/8/10)
• MATLAB (>2016b)
• A GLSL 3.3 compatible GPU.
• The OpenGL UI (glidviewer.exe)
• CUDA (Compute Capability > 3.5)

To run the software:

1. Start MATLAB
2. cd to the code folder
3. Call glid_main.m within MATLAB. This will automatically open the main GUI, and load the rex shape

The User Interface (main options):

4. For deformation, the p2p constraint can be edited by
   • adding P2P constraints by left clicking on the shape
   • moving the p2p target by dragging any p2p constraint
   • removing constraints by right clicking the p2p
5. GLID Deformer widget
   • Energy, isometric energy for the optimization
   • Solver, including mesh based AQP and SLIM, and harmonic subspace based Gradient Descent, LBFGS, Newton etc.
   • #samples, number of samples on the boundary for the boundary integral approximation
   • energy param, the paramter s for Exp Symmetric Dirichlet and AMIPS energies
   • Reset Shape, reset the shape to its original state (identity mapping)
   • Pause, paurse the iteration
   • Clear P2P: remove all the p2p constraints.

How to compile the binaries

The following libraries are needed to compile the code

1. OpenGL GUI (glidviewer.exe)

• Eigen http://eigen.tuxfamily.org
• AntTweakBar http://anttweakbar.sourceforge.net
• FreeGLUT http://freeglut.sourceforge.net

2. GPU-accelerated solver (cuHarmonic.mexw64)

• cub library https://nvlabs.github.io/cub/