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PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds (CVPR 2023)
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds
Jinyu Li, Chenxu Luo, Xiaodong Yang
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds, CVPR 2023
[Paper] [Poster]
Get Started
Installation
Please refer to INSTALL to set up environment and install dependencies (see detail in Dockerfile).
Data Preparation
Please follow the instructions in DATA.
Training and Evaluation
Please follow the instructions in RUN.
Main Results
nuScenes (Val)
| Model | mAP | NDS | Checkpoint |
|---|---|---|---|
| PillarNeXt-B | 62.5 | 68.8 | [Google Drive] [Baidu Cloud] |
Waymo Open Dataset
| 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 |
Citation
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}
}
Acknowledgement
We thank the authors for the multiple great open-sourced repos, including Det3D, CenterPoint and OpenPCDet.
License
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 [email protected].
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