Feed-Forward 3D Gaussian Splatting Compression with Long-Context Modeling

• URL: http://arxiv.org/abs/2512.00877v1
• Date: Sun, 30 Nov 2025 12:51:43 GMT
• Title: Feed-Forward 3D Gaussian Splatting Compression with Long-Context Modeling
• Authors: Zhening Liu, Rui Song, Yushi Huang, Yingdong Hu, Xinjie Zhang, Jiawei Shao, Zehong Lin, Jun Zhang,
• Abstract summary: 3DGS has emerged as a revolutionary 3D representation, but its substantial data size poses a major barrier to widespread adoption.<n>We propose a novel feed-forward 3DGS compression framework that effectively models long-range correlations.<n>Our method yields a $20times$ compression ratio for 3DGS in a feed-forward inference.
• Score: 30.948753429414648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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