(for DETR3D, BEVFormer, BEVDet, BEVDepth and Semantic Occupancy Prediction)
Multi-camera 3D perception has emerged as a prominent research field in autonomous driving, offering a viable and cost-effective alternative to LiDAR- based solutions. However, existing multi-camera algorithms primarily rely on monocular image pre-training, which overlooks the spatial and temporal correlations among different camera views. To address this limitation, we propose the first multi-camera unified pre-training framework called Occ-BEV, which involves initially reconstructing the 3D scene as the foundational stage and subsequently fine-tuning the model on downstream tasks. Specifically, a 3D decoder is designed for leveraging Bird’s Eye View (BEV) features from multi-view images to predict the 3D geometry occupancy to enable the model to capture a more comprehensive understanding of the 3D environment. One significant advantage of Occ-BEV is that it can utilize a vast amount of unlabeled image-LiDAR pairs for pre-training. The proposed multi-camera unified pre-training framework demonstrates promising results in key tasks such as multi-camera 3D object detection and semantic scene completion. When compared to monocular pre-training methods on the nuScenes dataset, Occ-BEV demonstrates a significant improvement of 2.0% in mAP and 2.0% in NDS for 3D object detection, as well as a 0.8% increase in mIOU for semantic scene completion.
| Backbone | Method | Pre-training | Lr Schd | NDS | mAP | Config |
|---|---|---|---|---|---|---|
| R101-DCN | BEVFormer | FCOS3D | 24ep | 51.7 | 41.6 | config/model |
| R101-DCN | BEVFormer | Occ-BEV | 24ep | 53.4 | 43.8 | config/pre-trained model/log |
If this work is helpful for your research, please consider citing the following BibTeX entry.
@article{occ-bev,
title={Occ-BEV: Multi-Camera Unified Pre-training via 3D Scene Reconstruction},
author={Chen Min, Xinli Xu, Dawei Zhao, Liang Xiao, Yiming Nie, and Bin Dai}
journal={arXiv preprint},
year={2023}
}
Many thanks to these excellent open source projects: