3D Occupancy Prediction

3D occupancy prediction aims to reconstruct a three-dimensional representation of a scene's occupied and unoccupied spaces, often including semantic labels, from visual data like images or videos. Current research emphasizes improving accuracy and efficiency through techniques like temporal fusion of multiple frames, height decoupling for better feature extraction, and the use of transformer-based architectures, often combined with neural radiance fields (NeRFs) or point cloud representations. This field is crucial for autonomous driving and robotics, enabling safer and more robust navigation by providing a detailed understanding of the surrounding environment.