AsySplat：面向长序列场景建模的高效非对称三维高斯泼溅技术

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）领域取得进展，但代价是产生了大量冗余计算。我们发现这种冗余可通过以下两点观察加以缓解：（i）高质量新视角合成并不严格依赖高精度几何建模；（ii）外观学习通常比几何重建更易实现。基于这些见解，我们提出一种解耦几何与外观建模的非对称架构。几何分支以大部分参数处理粗粒度令牌以完成多视角重建，而外观分支则以显著更少的参数对细粒度令牌进行操作以捕捉细节。两分支通过双边连接实现交互，从而为各自任务提供相互引导。这种任务感知非对称性减少了计算冗余，更合理地分配计算资源，进而提升参数效率，使更小规模的模型也能取得强劲性能。在32视角960P输入下，我们的模型性能与基于优化的方法相当，同时实现近800倍加速，并以更少的参数量与更低的训练/推理开销超越现有最先进可泛化模型的零样本性能，实现了整体效率提升。

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.