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 (see detail in Dockerfile).
Please follow the instructions in DATA.
Please follow the instructions in RUN.
| Model | mAP | NDS | Checkpoint |
|---|---|---|---|
| PillarNeXt-B | 62.5 | 68.8 | [Google Drive] [Baidu Cloud] |
| Split | #Frames | Veh L2 3D APH | Ped L2 3D APH | Cyc L2 3D APH |
|---|---|---|---|---|
| Val | 1 | 69.8 | 69.8 | 69.6 |
| Val | 3 | 72.4 | 75.2 | 75.7 |
| Test | 3 | 75.8 | 76.0 | 70.6 |
Please cite the following paper if this repo helps your research:
@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={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}
We thank the authors for the multiple great open-sourced repos, including Det3D, CenterPoint and OpenPCDet.
Copyright (C) 2023 QCraft. All rights reserved. Licensed under the CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International). The code is released for academic research use only. For commercial use, please contact business@qcraft.ai.