Semantic Occupancy

Semantic occupancy prediction aims to create detailed 3D models of a scene, including both geometric structure and semantic labels for each point, using various sensor inputs like cameras and LiDAR. Current research focuses on improving efficiency and reliability through novel architectures such as transformers and state space models, often incorporating techniques like volume rendering and uncertainty learning to enhance accuracy and robustness. This field is crucial for autonomous driving and robotics, enabling safer and more efficient navigation by providing rich, accurate representations of the surrounding environment.