Feed-Forward 3D Gaussian Splatting Compression with Long-Context Modeling
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911295205277696 |
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| author | Liu, Zhening Song, Rui Huang, Yushi Hu, Yingdong Zhang, Xinjie Shao, Jiawei Lin, Zehong Zhang, Jun |
| author_facet | Liu, Zhening Song, Rui Huang, Yushi Hu, Yingdong Zhang, Xinjie Shao, Jiawei Lin, Zehong Zhang, Jun |
| contents | 3D Gaussian Splatting (3DGS) has emerged as a revolutionary 3D representation. However, its substantial data size poses a major barrier to widespread adoption. While feed-forward 3DGS compression offers a practical alternative to costly per-scene per-train compressors, existing methods struggle to model long-range spatial dependencies, due to the limited receptive field of transform coding networks and the inadequate context capacity in entropy models. In this work, we propose a novel feed-forward 3DGS compression framework that effectively models long-range correlations to enable highly compact and generalizable 3D representations. Central to our approach is a large-scale context structure that comprises thousands of Gaussians based on Morton serialization. We then design a fine-grained space-channel auto-regressive entropy model to fully leverage this expansive context. Furthermore, we develop an attention-based transform coding model to extract informative latent priors by aggregating features from a wide range of neighboring Gaussians. Our method yields a $20\times$ compression ratio for 3DGS in a feed-forward inference and achieves state-of-the-art performance among generalizable codecs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_00877 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feed-Forward 3D Gaussian Splatting Compression with Long-Context Modeling Liu, Zhening Song, Rui Huang, Yushi Hu, Yingdong Zhang, Xinjie Shao, Jiawei Lin, Zehong Zhang, Jun Computer Vision and Pattern Recognition 3D Gaussian Splatting (3DGS) has emerged as a revolutionary 3D representation. However, its substantial data size poses a major barrier to widespread adoption. While feed-forward 3DGS compression offers a practical alternative to costly per-scene per-train compressors, existing methods struggle to model long-range spatial dependencies, due to the limited receptive field of transform coding networks and the inadequate context capacity in entropy models. In this work, we propose a novel feed-forward 3DGS compression framework that effectively models long-range correlations to enable highly compact and generalizable 3D representations. Central to our approach is a large-scale context structure that comprises thousands of Gaussians based on Morton serialization. We then design a fine-grained space-channel auto-regressive entropy model to fully leverage this expansive context. Furthermore, we develop an attention-based transform coding model to extract informative latent priors by aggregating features from a wide range of neighboring Gaussians. Our method yields a $20\times$ compression ratio for 3DGS in a feed-forward inference and achieves state-of-the-art performance among generalizable codecs. |
| title | Feed-Forward 3D Gaussian Splatting Compression with Long-Context Modeling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.00877 |