BEVSpread: Spread Voxel Pooling for Bird’s-Eye-View Representation in Vision-based Roadside 3D Object Detection
Wenjie Wang* · Yehao Lu* · Guangcong Zheng · Shuigen Zhan · Xiaoqing Ye · Zichang Tan · Jingdong Wang · Gaoang Wang · Xi Li
a. Install pytorch(v1.9.0).
b. Install mmcv-full==1.6.2 mmdet==2.28.2 mmsegmentation==0.30.0
c. Install mmdetection3d
git clone https://github.com/open-mmlab/mmdetection3d.git -b 1.0
cd mmdetection3d
pip install -e .
d. Install pypcd
git clone https://github.com/klintan/pypcd.git
cd pypcd
python setup.py install
d. Install requirements.
pip install -r requirements.txte. Install BEVSpread (gpu required).
python setup.py developDownload DAIR-V2X-I or Rope3D dataset from official website.
ln -s [single-infrastructure-side root] ./data/dair-v2x
ln -s [rope3d root] ./data/rope3d
python scripts/data_converter/dair2kitti.py --source-root data/dair-v2x-i --target-root data/dair-v2x-i-kitti
python scripts/data_converter/rope2kitti.py --source-root data/rope3d --target-root data/rope3d-kitti
python scripts/data_converter/visual_tools.py --data_root data/rope3d-kitti --demo_dir ./demo
The directory will be as follows.
data
├── dair-v2x-i
│ ├── velodyne
│ ├── image
│ ├── calib
│ ├── label
| └── data_info.json
├── dair-v2x-i-kitti
| ├── training
| | ├── calib
| | ├── label_2
| | └── images_2
| └── ImageSets
| ├── train.txt
| └── val.txt
├── rope3d
| ├── training
| ├── validation
| ├── training-image_2a
| ├── training-image_2b
| ├── training-image_2c
| ├── training-image_2d
| └── validation-image_2
├── rope3d-kitti
| ├── training
| | ├── calib
| | ├── denorm
| | ├── label_2
| | └── images_2
| └── map_token2id.json
|
...
python scripts/gen_info_dair.py
python scripts/gen_info_rope3d.py
Train BEVSpread with 8 GPUs
python [EXP_PATH] --amp_backend native -b 2 --gpus 8
Eval BEVSpread with 1 GPUs
python [EXP_PATH] --ckpt_path [CKPT_PATH] -e -b 2 --gpus 1
- DAIR-V2X-I Dataset
| Method | Config File | Range | Car | Pedestrain | Cyclist | model pth | ||||||
| 3D@0.5 | 3D@0.25 | 3D@0.25 | ||||||||||
| Easy | Mod. | Hard | Easy | Mod. | Hard | Easy | Mod. | Hard | ||||
| BEVSpread | R101_100m | [0, 102.4] | 79.15 | 66.86 | 66.92 | 46.64 | 44.61 | 44.73 | 63.15 | 63.55 | 63.94 | model |