MCSplat: Multi-View Photometric and Geometric Consistent Feed-Forward Gaussian Splatting for Driving Scenes

Driving-scene reconstruction via feed-forward Gaussian splatting from sparse multi-view images remains challenging due to two factors. First, cross-camera photometric inconsistency—caused by variations in exposure, ISP, and illumination—can be incorrectly absorbed into Gaussian geometry. Second, unreliable local geometric supervision arises from normal-based constraints near depth discontinuities. To address these issues, we present MCSplat, a multi-view consistent feed-forward Gaussian splatting framework for driving scenes. MCSplat constructs a canonical appearance space from depth-guided multi-camera correspondences to decouple geometry learning from camera-specific appearance. A multi-scale Bilateral Grid Transformer restores raw camera appearance from canonical renderings, enabling photometric supervision without contaminating Gaussian geometry. To improve local geometric reliability, we estimate Fisher-information based uncertainty from photometric sensitivity and use it to adaptively weight surface-oriented self-supervision. Experiments on the Waymo Dataset show that MCSplat achieves state-of-the-art performance on both the full validation set and an appearance-challenging subset with strong cross-camera photometric inconsistency, demonstrating improved reconstruction quality and more consistent novel-view geometry. Code will be released.