发表机构
HKUST; Huawei Noah’s Ark Lab; CityU（香港科技大学; 华为诺亚方舟实验室; 城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对长序列场景建模中3D高斯点云渲染的冗余计算问题，提出非对称架构解耦几何与外观建模，通过双边连接交互，减少计算冗余，提高参数效率，在32视图960P输入上大幅提升效率并超越零样本性能。
AI 中文摘要
近期可推广的3D高斯点云渲染模型推动了长序列新视图合成（NVS）发展，但存在大量冗余计算。基于高精度几何对高质量NVS非严格必需及外观学习通常比几何恢复容易的观察，提出非对称架构解耦几何与外观建模。几何分支处理粗粒度令牌用于多视图重建，外观分支处理细粒度令牌捕捉细节，二者通过双边连接交互。该任务感知非对称减少计算冗余，更合理分配计算，提高参数效率，使小模型性能强劲。在32视图960P输入上，模型匹配基于优化的方法且加速近800倍，超越零样本性能，减少训练/推理开销，实现整体效率提升。
英文摘要
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.
CommentsThe project page is at https://zhongyingji.github.io/asysplat/
