3D Semantic Occupancy

3D semantic occupancy prediction aims to create detailed 3D maps of a scene, labeling each point with its semantic class (e.g., car, pedestrian, road). Current research focuses on improving accuracy and efficiency through multi-sensor fusion (combining camera, LiDAR, and radar data), innovative model architectures like transformers and generative models (e.g., diffusion models), and techniques to address data sparsity and class imbalance. This technology is crucial for autonomous driving and robotics, enabling safer and more robust navigation and interaction with the environment by providing a richer understanding of the surrounding 3D space.