This report provides the mathematical details of the gsplat library, a modular toolbox for efficient differentiable Gaussian splatting, as proposed by Kerbl et al. It provides a self-contained reference for the computations involved in the forward and backward passes of differentiable Gaussian splatting. To facilitate practical usage and development, we provide a user friendly Python API that exposes each component of the forward and backward passes in rasterization at github.com/nerfstudio-project/gsplat .

翻译：本报告详细阐述了gsplat库的数学原理。gsplat库是一个模块化工具包，用于实现Kerbl等人提出的高效可微高斯泼溅方法。报告为可微高斯泼溅前向与反向传播过程中涉及的计算提供了自包含的参考文档。为便于实际应用与开发，我们在github.com/nerfstudio-project/gsplat上提供了用户友好的Python API，该API可公开访问栅格化过程中前向与反向传播的各个组件。