Papers › FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation
FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation
Zhiqi Li, Zhiding Yu, David Austin, Mingsheng Fang, Shiyi Lan, Jan Kautz, Jose M. Alvarez
This technical report summarizes the winning solution for the 3D Occupancy Prediction Challenge, which is held in conjunction with the CVPR 2023 Workshop on End-to-End Autonomous Driving and CVPR 23 Workshop on Vision-Centric Autonomous Driving Workshop. Our proposed solution FB-OCC builds upon FB-BEV, a cutting-edge camera-based bird's-eye view perception design using forward-backward projection. On top of FB-BEV, we further study novel designs and optimization tailored to the 3D occupancy prediction task, including joint depth-semantic pre-training, joint voxel-BEV representation, model scaling up, and effective post-processing strategies. These designs and optimization result in a state-of-the-art mIoU score of 54.19% on the nuScenes dataset, ranking the 1st place in the challenge track. Code and models will be released at: https://github.com/NVlabs/FB-BEV.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Prediction Of Occupancy Grid Maps | Occ3D-nuScenes | FB-OCC-K | mIoU | 52.79 | #2 of 8 | Archive leaderboard | report |
| Prediction Of Occupancy Grid Maps | Occ3D-nuScenes | FB-OCC-H | mIoU | 42.06 | #6 of 8 | Archive leaderboard | report |
| Prediction Of Occupancy Grid Maps | Occ3D-nuScenes | FB-OCC-G | mIoU | 40.69 | #7 of 8 | Archive leaderboard | report |
| Prediction Of Occupancy Grid Maps | Occ3D-nuScenes | CTF-Occ | mIoU | 28.53 | #8 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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