Jinyu Li, Chenxu Luo, Xiaodong Yang
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds, CVPR 2023
[Paper] [Poster]
Please refer to INSTALL to set up environment and install dependencies. Please refer to the Dockerfile for detail.
Please refer to DATA for detail.
Please refer to Training for detail.
| Model | mAP | NDS | checkpoint |
|---|---|---|---|
| PillarNeXt-B | 62.5 | 68.8 | [Google Drive] [Baidu Yunpan](7skt) |
| Split | #frames | Veh L2 | Ped L2 | Cyc L2 |
|---|---|---|---|---|
| val | 1 | 67.8 | 69.8 | 69.6 |
| val | 3 | 72.4 | 75.2 | 75.7 |
| test | 3 | 75.8 | 76.0 | 70.6 |
All numbers are 3D mAPH.
If you find this code useful in your research, please consider citing:
@inproceedings{li2023pillarnext,
title={PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds},
author={Li, Jinyu and Luo, Chenxu and Yang, Xiaodong},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={17567--17576},
year={2023}
}
This project is not possible without multiple great opensourced codebases. We list some notable examples below.
