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Mathematical Supplement for the $\texttt{gsplat}$ Library

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arxiv 2312.02121 v1 pith:COPQB2NC submitted 2023-12-04 cs.MS cs.CVcs.GRcs.NAmath.NA

Vickie Ye , Angjoo Kanazawa This is my paper · ORCID
classification cs.MScs.CVcs.GRcs.NAmath.NA
keywords gsplatbackwarddifferentiableforwardgaussianlibrarymathematicalpasses
verification ladder T0 review T1 audit T2 compute T3 formal
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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 .

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  1. Large Images are Gaussians: High-Quality Large Image Representation with Levels of 2D Gaussian Splatting

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A two-level 2D Gaussian splatting method with direct covariance optimization fits large images with more Gaussian points and higher PSNR than prior Gaussian-based image representation.

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