Differentiable Computational Geometry for 2D and 3D machine learning

11/22/2020
by   Yuanxin Zhong, et al.
1

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset