Mathematical Supplement for the $\texttt{gsplat}$ Library
Vickie Ye Angjoo Kanazawa
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 .