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BEVStereo

BEVStereo is a new multi-view 3D object detector using temporal stereo to enhance depth estimation.

【2022/09/22】 We released our paper on Arxiv.
【2022/08/24】 We submitted our result on nuScenes Detection Task and achieved the SOTA.

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

We use the same data format as BEVDepth, if you have processed it before, please skip.

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

Step 3. Prepare depth gt.

python scripts/gen_depth_gt.py

Train.

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

Eval.

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

Exp	Frames	EMA	CBGS	mAP	mATE	mASE	mAOE	mAVE	mAAE	NDS	weights
R50	key + sweep4			0.3427	0.6560	0.2784	0.5982	0.5347	0.2228	0.4423	github
R50	key + sweep4	√		0.3435	0.6585	0.2757	0.5792	0.5034	0.2163	0.4485	github
R50	key + key			0.3456	0.6589	0.2774	0.5500	0.4980	0.2278	0.4516	github
R50	key + key	√		0.3494	0.6671	0.2785	0.5606	0.4686	0.2295	0.4543	github
R50	key + key		√	0.3576	0.6071	0.2684	0.4157	0.3928	0.2021	0.4902	github
R50	key + key	√	√	0.3721	0.5980	0.2701	0.4381	0.3672	0.1898	0.4997	github

This project exists thanks to all the people who instruct. @Haotian-Zh @xavierwu95 @Tai-Wang