AsySplat: Efficient Asymmetric 3D Gaussian Splatting for Wide-Coverage Scene Modeling

Recent generalizable 3D Gaussian Splatting (3DGS) models have advanced wide-coverage 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. Extensive controlled experiments demonstrate that AsySplat achieves strong performance with markedly fewer parameters and reduced training and inference overhead. Combined with recent advances in generalizable 3DGS, our asymmetric design performs on par with LongLRM++ using less than 10% of its reconstruction FLOPs, and with MVP using less than 50% of its parameters and 25% of its Gaussians. Code and models will be released.