| title | AsySplat: Efficient Asymmetric 3D Gaussian Splatting for Long-Sequence Scene Modeling |
|---|---|
| authors | Yingji Zhong, Dave Zhenyu Chen, Fuzhao Ou, Youyu Chen, Zhihao Li, Lanqing Hong, Dan Xu |
| date | 2026-07-13 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.10995 |
| affiliation | HKUST |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Recent generalizable 3D Gaussian Splatting models have advanced long-sequence novel view synthesis (NVS), but at the cost of substantial redundant computation. We identify that the redundancy can be mitigated based on two observations: (i) high-precision geometry is not strictly required for high-quality NVS; (ii) appearance learning is generally easier than geometry recovery. Motivated by these insights, we propose an asymmetric architecture that decouples geometry and appearance modeling. The geometry branch processes coarse-grained tokens with most of the parameters for multi-view reconstruction, while the appearance branch operates on fine-grained tokens to capture details using significantly fewer parameters. The two branches interact through bilateral connections, enabling mutual guidance for their respective tasks. This task-aware asymmetry reduces the computational redundancy and allocates the computation more judiciously, thereby increasing parameter efficiency and enabling smaller models to achieve strong performance. On 32-view 960P inputs, our model matches optimization-based methods while delivering nearly 800x speedup, and surpasses the zero-shot performance of state-of-the-art generalizable models with markedly fewer parameters and reduced training/inference overhead, achieving an overall efficiency improvement. |
cite
@misc{zhong2026asysplat,
title={AsySplat: Efficient Asymmetric 3D Gaussian Splatting for Long-Sequence Scene Modeling},
author={Yingji Zhong and Dave Zhenyu Chen and Fuzhao Ou and Youyu Chen and Zhihao Li and Lanqing Hong and Dan Xu},
year={2026},
eprint={2607.10995},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.10995}
}