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arXiv · 2504.01844

BOGausS: Better Optimized Gaussian Splatting

Stéphane Pateux·Matthieu Gendrin·Luce Morin·Théo Ladune·Xiaoran Jiang
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Abstract

3D Gaussian Splatting (3DGS) proposes an efficient solution for novel view synthesis. Its framework provides fast and high-fidelity rendering. Although less complex than other solutions such as Neural Radiance Fields (NeRF), there are still some challenges building smaller models without sacrificing quality. In this study, we perform a careful analysis of 3DGS training process and propose a new optimization methodology. Our Better Optimized Gaussian Splatting (BOGausS) solution is able to generate models up to ten times lighter than the original 3DGS with no quality degradation, thus significantly boosting the performance of Gaussian Splatting compared to the state of the art.

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Stéphane Pateux, Matthieu Gendrin, Luce Morin, Théo Ladune, Xiaoran Jiang. 2025-04-02. BOGausS: Better Optimized Gaussian Splatting. https://arxiv.org/abs/2504.01844

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