CamoSplat: Temporal Appearance Modeling for 3D Gaussian Splatting in Autonomous Driving

Driving scenes contain local appearance changes over time, from gradual shading variations to abrupt traffic-light transitions. Capturing these changes with 3D Gaussian Splatting (3DGS) calls for temporal appearance control at the primitive level, yet current methods either keep per-Gaussian appearance fixed or tie its change to discrete per-frame codes. We introduce CamoSplat, which models a temporal appearance pattern for each Gaussian and controls how strongly it is expressed over time. These patterns come from a Mixture-of-Temporal-Bases (MoTB) that combines shared temporal functions with per-Gaussian weights, avoiding the overhead of separate per-Gaussian networks. To control when and how strongly each pattern acts, Morlet wavelet temporal gating modulates its amplitude across multiple temporal scales. The same wavelet control extends to visibility, where sensor-specific opacity wavelets add a temporal offset to a shared base opacity. CamoSplat achieves state-of-the-art photometric and LiDAR reconstruction on our temporal-variation splits from Waymo, Argoverse 2, and nuScenes, and the best overall results on standard benchmark scenes. Beyond these metrics, it precisely captures temporally localized appearance changes that prior methods collapse into ghosting or blur. We will release our code publicly.