4D Scene Understanding

4D scene understanding uses video and multi-view observations to recover scene geometry, track persistent objects, and interpret their motion and interactions. Common approaches include dynamic NeRFs and Gaussian splatting, point-cloud and occupancy models, scene graphs, and vision-language models that connect visual evidence with queries or actions. These methods support tasks such as novel-view rendering, occupancy forecasting, dynamic grounding, and robot planning, with evaluations on settings including nuScenes, Waymo, and HyperNeRF. A central challenge is balancing detailed, temporally consistent representations with efficient inference and reliable generalization, especially when observations are incomplete or language models must ground their answers in 3D motion.