AsySplat：用於長序列場景建模的高效非對稱3D高斯潑濺

摘要

近期可泛化的3D高斯泼溅模型在长序列新视角合成（NVS）方面取得了进展，但代价是大量冗余计算。我们基于两项观察认为该冗余可被缓解：(i) 高质量NVS并不严格需要高精度几何；(ii) 外观学习通常比几何重建更容易。受此启发，我们提出一种解耦几何与外观建模的非对称架构。几何分支以粗粒度令牌（token）处理多视角重建，并承载大部分参数；外观分支则以细粒度令牌运行，用显著更少的参数捕捉细节。两分支通过双向连接实现交互，从而为各自任务提供相互引导。这种任务感知的非对称性减少了计算冗余，使计算分配更为合理，进而提升参数效率，使较小模型也能达到强性能。在32视角960P输入下，我们的模型与基于优化的方法性能相当，同时实现近800倍加速；并以显著更少的参数及更低的训练/推理开销，超越最先进可泛化模型的零样本性能，实现整体效率提升。

English

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.