2020/11/22 by Yuanxin Zhong, Zhong, Yuanxin
Engineering · Computer Science · #Advanced Numerical Analysis Techniques #3D Shape Modeling and Analysis #Computational Geometry and Mesh Generation
paper · pdf · doi:10.48550/arxiv.2011.11134
With the growth of machine learning algorithms with geometry primitives, a high-efficiency library with differentiable geometric operators are desired. We present an optimized Differentiable Geometry Algorithm Library (DGAL) loaded with implementations of differentiable operators for geometric primitives like lines and polygons. The library is a header-only templated C++ library with GPU support. We discuss the internal design of the library and benchmark its performance on some tasks with other implementations.