# pillarnext

**Repository Path**: ericcai123/pillarnext

## Basic Information

- **Project Name**: pillarnext
- **Description**: fork from https://github.com/qcraftai/pillarnext
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-08-02
- **Last Updated**: 2024-10-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds


[Jinyu Li](https://konstantin5389.github.io/), [Chenxu Luo](https://chenxuluo.github.io/), [Xiaodong Yang](https://xiaodongyang.org/) <br>
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds, CVPR 2023 <br>
[[Paper]](https://arxiv.org/pdf/2305.04925.pdf) [[Poster]](docs/poster.pdf) 

<p align="left"> 
 <img src='docs/teaser_figure.png' height="410px"/> 
</p>

## Getting Started

### Installation
Please refer to [INSTALL](docs/INSTALL.md) to set up environment and install dependencies (see detail in [Dockerfile](docker/Dockerfile)).

### Data Preparation
Please follow the instructions in [DATA](docs/DATA.md). 

### Training and Evaluation 
Please follow the instructions in [RUN](docs/RUN.md).


## Main Results
### nuScenes (Val)
| Model |  mAP  |  NDS | Checkpoint
| ------| -----| ---- | -------------|
 | PillarNeXt-B | 62.5 | 68.8	| [[Google Drive]](https://drive.google.com/file/d/16abCgt-yhRGnYHQ7M259yGMO0IRYpZ8o/view?usp=drive_link) [[Baidu Cloud]](https://pan.baidu.com/s/1TRsjgN1ys5-mAxM70l4hog?pwd=7skt)

### Waymo Open Dataset 
|Split | #Frames | Veh L2 3D APH | Ped L2 3D APH | Cyc L2 3D APH | 
| ---------| ---------|---------|---------|---------|
| Val | 1 | 67.8 | 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.
* [Det3D](https://github.com/poodarchu/Det3D)
* [CenterPoint](https://github.com/tianweiy/CenterPoint)
* [OpenPCDet](https://github.com/open-mmlab/OpenPCDet)

## License
Copyright (C) 2023 QCraft. All rights reserved. Licensed under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) (Attribution-NonCommercial-ShareAlike 4.0 International). The code is released for academic research use only. For commercial use, please contact [business@qcraft.ai](business@qcraft.ai).