{"ID":2827074,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.17528","arxiv_id":"2512.17528","title":"Voxel-GS: Quantized Scaffold Gaussian Splatting Compression with Run-Length Coding","abstract":"Substantial Gaussian splatting format point clouds require effective compression. In this paper, we propose Voxel-GS, a simple yet highly effective framework that departs from the complex neural entropy models of prior work, instead achieving competitive performance using only a lightweight rate proxy and run-length coding. Specifically, we employ a differentiable quantization to discretize the Gaussian attributes of Scaffold-GS. Subsequently, a Laplacian-based rate proxy is devised to impose an entropy constraint, guiding the generation of high-fidelity and compact reconstructions. Finally, this integer-type Gaussian point cloud is compressed losslessly using Octree and run-length coding. Experiments validate that the proposed rate proxy accurately estimates the bitrate of run-length coding, enabling Voxel-GS to eliminate redundancy and optimize for a more compact representation. Consequently, our method achieves a remarkable compression ratio with significantly faster coding speeds than prior art. The code is available at https://github.com/zb12138/VoxelGS.","short_abstract":"Substantial Gaussian splatting format point clouds require effective compression. In this paper, we propose Voxel-GS, a simple yet highly effective framework that departs from the complex neural entropy models of prior work, instead achieving competitive performance using only a lightweight rate proxy and run-length co...","url_abs":"https://arxiv.org/abs/2512.17528","url_pdf":"https://arxiv.org/pdf/2512.17528v1","authors":"[\"Chunyang Fu\",\"Xiangrui Liu\",\"Shiqi Wang\",\"Zhu Li\"]","published":"2025-12-19T12:51:40Z","proceeding":"cs.MM","tasks":"[\"cs.MM\"]","methods":"[]","has_code":false,"code_links":[{"ID":605783,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2827074,"paper_url":"https://arxiv.org/abs/2512.17528","paper_title":"Voxel-GS: Quantized Scaffold Gaussian Splatting Compression with Run-Length Coding","repo_url":"https://github.com/zb12138/VoxelGS","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}