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:
this https URL

本文简述了在 3D 占用预测挑战中获胜的解决方案，该方案基于 FB-BEV，并在其基础上进一步研究了针对 3D 占用预测任务的新设计和优化。最终结果表明，该方案在 nuScenes 数据集上获得了最先进的 mIoU 得分，排名挑战榜的第一位。