Provable Pruning for Efficient 3D Gaussian Splatting via Coresets

Summary · qwen2.5:32b

The study introduces a provable method for creating coresets in 3D Gaussian Splatting (3DGS) that significantly reduces the number of Gaussians required while preserving rendering quality, with guarantees dependent on the resolution. This method samples Gaussians based on their sensitivity scores, demonstrating state-of-the-art performance under aggressive compression and minimal recovery compute conditions. Empirical results show superior performance in prune-only scenarios and short finetuning regimes compared to existing heuristic methods.

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Share this article about a new coreset construction theorem for 3D Gaussian Splatting that could potentially improve real-time novel-view synthesis by reducing the number of Gaussians without affecting quality.

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arXiv:2607.02721v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) enables high-quality real-time novel-view synthesis, but practical scenes often contain millions of Gaussians, making compression essential for deployment on limited hardware. Existing reduction methods are effective but mostly heuristic: they provide no multiplicative approximation guarantee for the rendered objective, and thus rely heavily on costly post-pruning finetuning to recover quality. We ask a basic question: can a 3DGS scene be provably replaced by a much smaller weighted subset (coreset) while preserving the objective of interest? We first show that, in the unrestricted setting, no non-trivial multiplicative 3DGS coreset exists. We then show that multiplicative guarantees are not impossible, but resolution-dependent. For a prescribed rendering resolution, such as representative views or grids of views/rays, we provide the first weighted coreset construction theorem for 3DGS. The construction samples Gaussians by sensitivity: provable importance scores measuring each Gaussian's role in the full-scene objective. Finally, under explicit validity and log-transmittance stability assumptions, we turn this objective guarantee into a rendering guarantee. Empirically, our method is strongest where deployment needs it most: aggressive compression with no or minimal recovery compute. In prune-only and very short finetuning regimes, it achieves state-of-the-art performance, showing that principled importance estimation can be both theoretically meaningful and practically useful. Open-source code is available at https://github.com/waseem-m/3dgs_provable_coresets.