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arXiv · 2307.01492

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
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Abstract

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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Zhiqi Li, Zhiding Yu, David Austin, Mingsheng Fang, Shiyi Lan, Jan Kautz, Jose M. Alvarez. 2023-07-04. FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation. https://arxiv.org/abs/2307.01492

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