3D Occupancy

3D occupancy prediction aims to create a detailed, three-dimensional representation of a scene's occupied and unoccupied spaces, often incorporating semantic information about objects within the scene. Current research focuses on improving the efficiency and accuracy of these predictions, exploring various model architectures including transformer-based networks, diffusion models, and those leveraging multi-sensor fusion (e.g., cameras, LiDAR, radar) to overcome limitations of individual sensor modalities. This technology is crucial for autonomous driving and robotics, enabling safer and more robust navigation by providing a comprehensive understanding of the surrounding environment.