论文标题
2D和3D机器学习的可微分计算几何形状
Differentiable Computational Geometry for 2D and 3D machine learning
论文作者
论文摘要
随着机器学习算法的生长，几何原始词是需要具有可区分几何运算符的高效率库。我们提出了一个优化的可区分几何算法库（DGAL），其中包含用于线条和多边形等几何原语的可区分运算符的实现。该库是具有GPU支持的仅标题模板的C ++库。我们讨论了图书馆的内部设计，并通过其他实现在某些任务上进行基准效果。
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.
