In this technical report, we present our solution, named UniOCC, for the
Vision-Centric 3D occupancy prediction track in the nuScenes Open Dataset
Challenge at CVPR 2023. Existing methods for occupancy prediction primarily
focus on optimizing projected features on 3D volume space using 3D occupancy
labels. However, the generation process of these labels is complex and
expensive (relying on 3D semantic annotations), and limited by voxel
resolution, they cannot provide fine-grained spatial semantics. To address this
limitation, we propose a novel Unifying Occupancy (UniOcc) prediction method,
explicitly imposing spatial geometry constraint and complementing fine-grained
semantic supervision through volume ray rendering. Our method significantly
enhances model performance and demonstrates promising potential in reducing
human annotation costs. Given the laborious nature of annotating 3D occupancy,
we further introduce a Depth-aware Teacher Student (DTS) framework to enhance
prediction accuracy using unlabeled data. Our solution achieves 51.27\% mIoU on
the official leaderboard with single model, placing 3rd in this challenge.

本技术报告介绍了 UniOCC 解决方案，它采用空间几何约束和体积光线渲染来提高 3D 占用预测性能，从而在 CVPR2023 的 nuScenes Open Dataset Challenge 中获得了 51.27％的 mIoU，在 3D 占用注释成本方面具有很大的潜力。