RPEA

Official PyTorch implementation of RPEA: A Residual Path Network with Efficient Attention for 3D Pedestrian Detection from LiDAR Point Clouds. [Paper]

News

• (2024-03-23) 🔥 We release the code of RPEA.
• (2024-02-15) RPEA is accepted by Expert Systems With Applications 2024.
• (2023-07-04) 🏆 RPEA ranks first on JRDB 2022 3D Pedestrian Detection Leaderboard.
• (2023-07-04) 🏆 RPEA ranks first on JRDB 2019 3D Pedestrian Detection Leaderboard.

3D Pedestrian Detection Results

JRDB Leaderboard (validation set)

Model	AP@0.3	AP@0.5	AP@0.7	Checkpoint
JRDB 2022	72.554%	42.758%	5.047%	RPEA_JRDB2022.pth
JRDB 2019	73.486%	43.409%	5.773%	RPEA_JRDB2019.pth

JRDB Leaderboard

Model	OSPA@IoU	AP@0.3	AP@0.5	AP@0.7
JRDB 2022	0.707	74.445%	45.413%	5.166%
JRDB 2019	0.572	76.905%	46.076%	5.296%

Requirements

• python==3.9
• PyTorch==1.13.1
• cuda==11.6
• torchsparse==1.2.0 (link)

python setup.py develop
cd lib/iou3d
python setup.py develop
cd ../jrdb_det3d_eval
python setup.py develop

JRDB dataset

Download JRDB dataset under PROJECT/data.

python train.py [--evaluation] --cfg PATH_TO_CFG [--ckpt PATH_TO_CKPT]

#convert_labels_to_KITTI
python lib/jrdb_devkit/detection_eval/convert_labels_to_KITTI.py
#train
python train.py --cfg ./jrdb19.yaml
#validation
python train.py --cfg ./jrdb19.yaml --evaluation --ckpt ckpt_e40_train.pth

Visualize the predicted results.

Citation

@article{guang2024rpea,
  title={RPEA: A Residual Path Network with Efficient Attention for 3D pedestrian detection from LiDAR point clouds},
  author={Guang, Jinzheng and Hu, Zhengxi and Wu, Shichao and Zhang, Qianyi and Liu, Jingtai},
  journal={Expert Systems with Applications},
  volume = {249},
  pages={123497},
  year={2024},
  publisher={Elsevier}
}

Acknowledgement

• Person_MinkUNet (link)
• PiFeNet (link)
• torchsparse (link)
• PointRCNN (link)

Contact Information

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