Zeyu Wang, Dingwen Li, Chenxu Luo, Cihang Xie, Xiaodong Yang
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation, ICCV 2023
Please refer to INSTALL to set up the environment and install the dependencies (see more details in Dockerfile).
Please follow the instructions in DATA.
Please follow the instructions in RUN.
BEVDepth and BEVFormer are respectively used in this repo as CNNs and Transformers based students to exemplify the improvement by DistillBEV.
- Backbone: ResNet-50 pre-trained on ImageNet-1K
| Teacher | Student | mAP | NDS | Checkpoints |
|---|---|---|---|---|
| - | BEVDepth | 36.4 | 48.4 | Google Drive Baidu Cloud |
| CenterPoint | BEVDepth | 39.0 | 50.6 | Google Drive Baidu Cloud |
| MVP | BEVDepth | 40.3 | 51.0 | Google Drive Baidu Cloud |
| Teacher | Student | mAP | NDS | Checkpoints |
|---|---|---|---|---|
| - | BEVFormer | 32.3 | 43.4 | Google Drive Baidu Cloud |
| CenterPoint | BEVFormer | 35.9 | 46.8 | Google Drive Baidu Cloud |
| MVP | BEVFormer | 36.7 | 47.6 | Google Drive Baidu Cloud |
Please cite the following paper if this repo helps your research:
@InProceedings{Wang_2023_ICCV,
author = {Wang, Zeyu and Li, Dingwen and Luo, Chenxu and Xie, Cihang and Yang, Xiaodong},
title = {DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation},
booktitle = {IEEE/CVF International Conference on Computer Vision (ICCV)},
year = {2023},
}
We thank the authors for the multiple great open-sourced repos, including MMDetection3D, CenterPoint, BEVDet, BEVDepth and BEVFormer.
Copyright (C) 2023 QCraft. All rights reserved. Licensed under the CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International). The code is released for academic research use only. For commercial use, please contact business@qcraft.ai.