Jing Wang, Qi Zhang, Pengpeng Yu, Yulan Guo, Tai Qin, Yueru Chen, Fei Song
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3D Gaussian Splatting (3DGS) enables high-quality novel-view synthesis but requires substantial storage. Existing compression methods often rely on spatial context modeling over irregular 3D representations, increasing the complexity of training and coding. Meanwhile, floating-point context inference can introduce numerical inconsistencies across platforms, causing entropy-decoding failures. To address these practical challenges, we propose COSA-GS, which constructs context without spatial aggregation through anchor-wise causal factorization. Specifically, we use geometry context derived from each anchor's coordinates to model a compact learnable anchor latent. The anchor latent is then fused with the geometry context to form an anchor context for attribute coding. The resulting context model features a simple architecture composed solely of linear transformations and activations. We train COSA-GS using rate--distortion optimization with adaptive Gaussian pruning. Further, we develop quantization-aware training and integer inference for the context model to achieve bit-exact consistency of entropy-decoded symbols across platforms. Experiments demonstrate that COSA-GS achieves state-of-the-art compression performance while retaining fast and consistent cross-platform decoding, providing a simple yet effective framework for practical 3DGS compression. Code is available at https://github.com/pengpeng-yu/COSA-GS.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2609.30245")
get_code_for_paper("2609.30245")
have("2609.30245")
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curl https://syntology.ai/api/ran/2609.30245.json
A badge for a README (the split and the date, never a ratio):
[](https://syntology.ai/paper/2609.30245)
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