RoadsideSplat: Robust 3D Gaussian Reconstruction from Monocular Roadside Surveillance

Abstract

Reconstructing dynamic roads from roadside traffic surveillance cameras is crucial for smart cities and digital twin applications. While the latest monocular depth estimation methods demonstrate strong performance, they exhibit instability in roadside scenarios. Existing reconstruction approaches for autonomous driving scenes predominantly adopt vehicle-mounted perspectives, accumulating vehicle point clouds from per-frame depth maps using 3D bounding boxes. These point clouds are used to initialize the center positions and colors of 3D Gaussians to improve reconstruction performance. However, the compressed depth discrepancy between vehicles and road surfaces in roadside views leads to model confusion between vehicle and background depth estimations. To address these challenges, we propose a robust reconstruction framework based on a single fixed RGB traffic camera. Differing from conventional frame-wise depth prediction followed by 3D box-based accumulation, our method processes masked vehicle fore-ground sequences through existing models, directly predicting complete vehicle point clouds via local feature matching and global alignment while iteratively refining 3D boxes to enhance reconstruction quality. Leveraging the explicit nature of 3D Gaussians for scene editing, we introduce simple yet effective road constraints to mitigate penetration artifacts during scene manipulation. Extensive evaluations on the TUMTraf-V2X and RCooper datasets under monocular roadside settings validate the effectiveness of our approach.