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.

中文翻译：

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用于2D和3D机器学习的可微计算几何

随着带有几何基元的机器学习算法的增长，需要具有可区分几何运算符的高效库。我们提供了优化的可微分几何算法库（DGAL），其中载有诸如线和多边形之类的几何图元的可微分运算符的实现。该库是具有GPU支持的仅标头模板化C ++库。我们讨论了库的内部设计，并在使用其他实现的某些任务上对它的性能进行了基准测试。