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BEVDepth

BEVDepth is a new 3D object detector with a trustworthy depth estimation. For more details, please refer to our paper on Arxiv.

BEVStereo

BEVStereo is a new multi-view 3D object detector using temporal stereo to enhance depth estimation. <img src="assets/bevstereo.png" width="1000" >

MatrixVT

MatrixVT is a novel View Transformer for BEV paradigm with high efficiency and without customized operators. For more details, please refer to our paper on Arxiv. Try MatrixVT on CPU by run this file ! <img src="assets/matrixvt.jpg" width="1000" >

Updates!!

【2022/12/06】 We released our new View Transformer (MatrixVT), the paper is on Arxiv.
【2022/11/30】 We updated our paper(BEVDepth) on Arxiv.
【2022/11/18】 Both BEVDepth and BEVStereo were accepted by AAAI'2023.
【2022/09/22】 We released our paper(BEVStereo) on Arxiv.
【2022/08/24】 We submitted our result(BEVStereo) on nuScenes Detection Task and achieved the SOTA.
【2022/06/23】 We submitted our result(BEVDepth) without extra data on nuScenes Detection Task and achieved the SOTA.
【2022/06/21】 We released our paper(BEVDepth) on Arxiv.
【2022/04/11】 We submitted our result(BEVDepth) on nuScenes Detection Task and achieved the SOTA.

Quick Start

Installation

Step 0. Install pytorch(v1.9.0).

Step 1. Install MMDetection3D(v1.0.0rc4).

Step 2. Install requirements.

pip install -r requirements.txt

Step 3. Install BEVDepth(gpu required).

python setup.py develop

Data preparation

Step 0. Download nuScenes official dataset.

Step 1. Symlink the dataset root to ./data/.

ln -s [nuscenes root] ./data/

The directory will be as follows.

BEVDepth
├── data
│   ├── nuScenes
│   │   ├── maps
│   │   ├── samples
│   │   ├── sweeps
│   │   ├── v1.0-test
|   |   ├── v1.0-trainval

Step 2. Prepare infos.

python scripts/gen_info.py

Tutorials

Train.

python [EXP_PATH] --amp_backend native -b 8 --gpus 8

Eval.

python [EXP_PATH] --ckpt_path [CKPT_PATH] -e -b 8 --gpus 8

Benchmark

Exp	EMA	CBGS	mAP	mATE	mASE	mAOE	mAVE	mAAE	NDS	weights
BEVDepth			0.3304	0.7021	0.2795	0.5346	0.5530	0.2274	0.4355	github
BEVDepth	√		0.3329	0.6832	0.2761	0.5446	0.5258	0.2259	0.4409	github
BEVDepth		√	0.3484	0.6159	0.2716	0.4144	0.4402	0.1954	0.4805	github
BEVDepth	√	√	0.3589	0.6119	0.2692	0.5074	0.4086	0.2009	0.4797	github
BEVStereo			0.3456	0.6589	0.2774	0.5500	0.4980	0.2278	0.4516	github
BEVStereo	√		0.3494	0.6671	0.2785	0.5606	0.4686	0.2295	0.4543	github
BEVStereo			0.3427	0.6560	0.2784	0.5982	0.5347	0.2228	0.4423	github
BEVStereo	√		0.3435	0.6585	0.2757	0.5792	0.5034	0.2163	0.4485	github
BEVStereo		√	0.3576	0.6071	0.2684	0.4157	0.3928	0.2021	0.4902	github
BEVStereo	√	√	0.3721	0.5980	0.2701	0.4381	0.3672	0.1898	0.4997	github

FAQ

EMA

The results are different between evaluation during training and evaluation from ckpt.

Due to the working mechanism of EMA, the model parameters saved by ckpt are different from the model parameters used in the training stage.

EMA exps are unable to resume training from ckpt.

We used the customized EMA callback and this function is not supported for now.

Cite BEVDepth & BEVStereo & MatrixVT

If you use BEVDepth and BEVStereo in your research, please cite our work by using the following BibTeX entry:

 @article{li2022bevdepth,
  title={BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection},
  author={Li, Yinhao and Ge, Zheng and Yu, Guanyi and Yang, Jinrong and Wang, Zengran and Shi, Yukang and Sun, Jianjian and Li, Zeming},
  journal={arXiv preprint arXiv:2206.10092},
  year={2022}
}
@article{li2022bevstereo,
  title={Bevstereo: Enhancing depth estimation in multi-view 3d object detection with dynamic temporal stereo},
  author={Li, Yinhao and Bao, Han and Ge, Zheng and Yang, Jinrong and Sun, Jianjian and Li, Zeming},
  journal={arXiv preprint arXiv:2209.10248},
  year={2022}
}
@article{zhou2022matrixvt,
  title={MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perception},
  author={Zhou, Hongyu and Ge, Zheng and Li, Zeming and Zhang, Xiangyu},
  journal={arXiv preprint arXiv:2211.10593},
  year={2022}
}