Dynamic Street-Scene Reconstruction with Semantic Priors and Temporal Constraints

Dynamic street-scene reconstruction from sparse viewpoints over long temporal spans is challenged by temporal instability, ghosting near occlusions, and background drift. This paper presents SPT-Gauss (Semantic Prior and Temporal constraint-enhanced Gaussian splatting), a Gaussian-splatting framework that improves dynamic reconstruction without object-level annotations by combining dense semantic priors with lightweight, parameter-level temporal regularization. SPT-Gauss distills per-pixel semantic features from a frozen 2D foundation model into 4D Gaussian primitives, estimates static and dynamic regions via a dual-evidence motion mask, and regularizes temporal parameters through a semantic-guided velocity constraint and a static-lifetime prior to suppress spurious background motion. Experiments on the Waymo Open Dataset and KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) show consistent improvements over representative baselines in both 4D reconstruction and novel-view synthesis, with reduced temporal artifacts and improved fidelity in motion-challenging regions.