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Robotic perception requires the modeling of both 3D geometry and semantics.Existing methods typically focus on estimating 3D bounding boxes, neglectingfiner geometric details and struggling to handle general, out-of-vocabularyobjects. 3D occupancy prediction, which estimates the detailed occupancy statesand semantics of a scene, is an emerging task to overcome these limitations. Tosupport 3D occupancy prediction, we develop a label generation pipeline thatproduces dense, visibility-aware labels for any given scene. This pipelinecomprises three stages: voxel densification, occlusion reasoning, andimage-guided voxel refinement. We establish two benchmarks, derived from theWaymo Open Dataset and the nuScenes Dataset, namely Occ3D-Waymo andOcc3D-nuScenes benchmarks. Furthermore, we provide an extensive analysis of theproposed dataset with various baseline models. Lastly, we propose a new model,dubbed Coarse-to-Fine Occupancy (CTF-Occ) network, which demonstrates superiorperformance on the Occ3D benchmarks. The code, data, and benchmarks arereleased at https://tsinghua-mars-lab.github.io/Occ3D/.

Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving