CSGaussian: Progressive Rate-Distortion Compression and Segmentation for 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2601.12814v1
• Date: Mon, 19 Jan 2026 08:21:45 GMT
• Title: CSGaussian: Progressive Rate-Distortion Compression and Segmentation for 3D Gaussian Splatting
• Authors: Yu-Jen Tseng, Chia-Hao Kao, Jing-Zhong Chen, Alessandro Gnutti, Shao-Yuan Lo, Yen-Yu Lin, Wen-Hsiao Peng,
• Abstract summary: We present the first unified framework for rate-distortion-optimized compression and segmentation of 3D Gaussian Splatting (3DGS)<n>Inspired by recent advances in rate-distortion-optimized 3DGS compression, this work integrates semantic learning into the compression pipeline to support decoder-side applications.<n>Our scheme features a lightweight implicit neural representation-based hyperprior, enabling efficient entropy coding of both color and semantic attributes.
• Score: 57.73006852239138
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present the first unified framework for rate-distortion-optimized compression and segmentation of 3D Gaussian Splatting (3DGS). While 3DGS has proven effective for both real-time rendering and semantic scene understanding, prior works have largely treated these tasks independently, leaving their joint consideration unexplored. Inspired by recent advances in rate-distortion-optimized 3DGS compression, this work integrates semantic learning into the compression pipeline to support decoder-side applications--such as scene editing and manipulation--that extend beyond traditional scene reconstruction and view synthesis. Our scheme features a lightweight implicit neural representation-based hyperprior, enabling efficient entropy coding of both color and semantic attributes while avoiding costly grid-based hyperprior as seen in many prior works. To facilitate compression and segmentation, we further develop compression-guided segmentation learning, consisting of quantization-aware training to enhance feature separability and a quality-aware weighting mechanism to suppress unreliable Gaussian primitives. Extensive experiments on the LERF and 3D-OVS datasets demonstrate that our approach significantly reduces transmission cost while preserving high rendering quality and strong segmentation performance.

Related papers

• LL-GaussianImage: Efficient Image Representation for Zero-shot Low-Light Enhancement with 2D Gaussian Splatting [26.287424110093113]
  2D Gaussian Splatting (2DGS) is an emerging explicit scene representation method with significant potential for image compression.<n>Existing low-light enhancement algorithms operate predominantly within the pixel domain.<n>We propose LL-GaussianImage, the first zero-shot unsupervised framework designed for low-light enhancement directly within the 2DGS compressed representation domain.
  arXiv  Detail & Related papers  (2026-01-22T09:01:08Z)
• Leveraging Learned Image Prior for 3D Gaussian Compression [47.29061692878941]
  We introduce a novel 3DGS compression framework that leverages the powerful representational capacity of learned image priors to recover compression-induced quality degradation.<n>Our framework is designed to be compatible with existing Gaussian compression methods, making it broadly applicable across different baselines.
  arXiv  Detail & Related papers  (2025-10-16T14:10:02Z)
• D-FCGS: Feedforward Compression of Dynamic Gaussian Splatting for Free-Viewpoint Videos [12.24209693552492]
  Free-viewpoint video (FVV) enables immersive 3D experiences, but efficient compression of dynamic 3D representations remains a major challenge.<n>This paper presents Feedforward Compression of Dynamic Gaussian Splatting (D-FCGS), a novel feedforward framework for compressing temporally correlated Gaussian point cloud sequences.<n> Experiments show that it matches the rate-distortion performance of optimization-based methods, achieving over 40 times compression in under 2 seconds.
  arXiv  Detail & Related papers  (2025-07-08T10:39:32Z)
• 3DGS Compression with Sparsity-guided Hierarchical Transform Coding [19.575833741231953]
  Sparsity-guided Hierarchical Transform Coding (SHTC) is first end-to-end optimized transform coding framework for 3DGS compression.<n>SHTC jointly optimize the 3DGS, transforms and a lightweight context model.<n>This novel design significantly improves R-D performance with minimal additional parameters and computational overhead.
  arXiv  Detail & Related papers  (2025-05-28T22:17:24Z)
• CompGS++: Compressed Gaussian Splatting for Static and Dynamic Scene Representation [60.712165339762116]
  CompGS++ is a novel framework that leverages compact Gaussian primitives to achieve accurate 3D modeling.<n>Our design is based on the principle of eliminating redundancy both between and within primitives.<n>Our implementation will be made publicly available on GitHub to facilitate further research.
  arXiv  Detail & Related papers  (2025-04-17T15:33:01Z)
• 4DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video [56.04182926886754]
  3D Gaussian Splatting (3DGS) has substantial potential for enabling photorealistic Free-Viewpoint Video (FVV) experiences.<n>Existing methods typically handle dynamic 3DGS representation and compression separately, motion information and the rate-distortion trade-off during training.<n>We propose 4DGC, a rate-aware 4D Gaussian compression framework that significantly reduces storage size while maintaining superior RD performance for FVV.
  arXiv  Detail & Related papers  (2025-03-24T08:05:27Z)
• CAT-3DGS: A Context-Adaptive Triplane Approach to Rate-Distortion-Optimized 3DGS Compression [10.869104603083676]
  3D Gaussian Splatting (3DGS) has recently emerged as a promising 3D representation.<n>The needs to compress and transmit the 3DGS representation to the remote side are overlooked.<n>This new application calls for rate-distortion-optimized 3DGS compression.
  arXiv  Detail & Related papers  (2025-03-01T05:42:52Z)
• ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model [77.71796503321632]
  We introduce a context model in the anchor level for 3DGS representation, yielding an impressive size reduction of over 100 times compared to vanilla 3DGS. Our work pioneers the context model in the anchor level for 3DGS representation, yielding an impressive size reduction of over 100 times compared to vanilla 3DGS and 15 times compared to the most recent state-of-the-art work Scaffold-GS.
  arXiv  Detail & Related papers  (2024-05-31T09:23:39Z)
• CompGS: Efficient 3D Scene Representation via Compressed Gaussian Splatting [68.94594215660473]
  We propose an efficient 3D scene representation, named Compressed Gaussian Splatting (CompGS) We exploit a small set of anchor primitives for prediction, allowing the majority of primitives to be encapsulated into highly compact residual forms. Experimental results show that the proposed CompGS significantly outperforms existing methods, achieving superior compactness in 3D scene representation without compromising model accuracy and rendering quality.
  arXiv  Detail & Related papers  (2024-04-15T04:50:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.