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BOGausS: Better Optimized Gaussian Splatting
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BOGausS: Better Optimized Gaussian Splatting
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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.
Forward citations
Cited by 2 Pith papers
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DP-Splat: Bayesian Nonparametric Complexity Control for Gaussian Splatting
Truncated stick-breaking DP (and sparse Dirichlet) priors give adaptive component counts in conjugate VBGS-style Gaussian splatting, with a corrected truncation bound and documented reversal of asymptotic ˆK ordering ...
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Smol-GS: Compact Representations for Abstract 3D Gaussian Splatting
A 3D Gaussian Splatting compression method that stores coordinates in an occupancy octree and represents appearance with 8-d learned features, reaching ~4.7-6.4 MB per scene at near-SOTA quality.
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