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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
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2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2601.12814 |
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| _version_ | 1866912832042303488 |
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| author | Tseng, Yu-Jen Kao, Chia-Hao Chen, Jing-Zhong Gnutti, Alessandro Lo, Shao-Yuan Lin, Yen-Yu Peng, Wen-Hsiao |
| author_facet | Tseng, Yu-Jen Kao, Chia-Hao Chen, Jing-Zhong Gnutti, Alessandro Lo, Shao-Yuan Lin, Yen-Yu Peng, Wen-Hsiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12814 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CSGaussian: Progressive Rate-Distortion Compression and Segmentation for 3D Gaussian Splatting Tseng, Yu-Jen Kao, Chia-Hao Chen, Jing-Zhong Gnutti, Alessandro Lo, Shao-Yuan Lin, Yen-Yu Peng, Wen-Hsiao Computer Vision and Pattern Recognition 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. |
| title | CSGaussian: Progressive Rate-Distortion Compression and Segmentation for 3D Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.12814 |