metadata
language:
- Python
thumbnail: https://github.com/dvlab-research/FocalsConv
tags:
- Sparse Conv
- 3D Object Detection
datasets:
- KITTI
- nuScenes
Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral)
This is the official implementation of Focals Conv (CVPR 2022), a new sparse convolution design for 3D object detection (feasible for both lidar-only and multi-modal settings). For more details, please refer to:
Focal Sparse Convolutional Networks for 3D Object Detection [Paper] [Github]
Yukang Chen, Yanwei Li, Xiangyu Zhang, Jian Sun, Jiaya Jia
KITTI dataset
| Car@R11 | Car@R40 | download | |
|---|---|---|---|
| PV-RCNN + Focals Conv | 83.91 | 85.20 | Google | Baidu (key: m15b) |
| PV-RCNN + Focals Conv (multimodal) | 84.58 | 85.34 | Google | Baidu (key: ie6n) |
| Voxel R-CNN (Car) + Focals Conv (multimodal) | 85.68 | 86.00 | Google | Baidu (key: tnw9) |
nuScenes dataset
| mAP | NDS | download | |
|---|---|---|---|
| CenterPoint + Focals Conv (multi-modal) | 63.86 | 69.41 | Google | Baidu (key: 01jh) |
| CenterPoint + Focals Conv (multi-modal) - 1/4 data | 62.15 | 67.45 | Google | Baidu (key: 6qsc) |
Citation
If you find this project useful in your research, please consider citing:
@inproceedings{focalsconv-chen,
title={Focal Sparse Convolutional Networks for 3D Object Detection},
author={Chen, Yukang and Li, Yanwei and Zhang, Xiangyu and Sun, Jian and Jia, Jiaya},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2022}
}
License
This project is released under the Apache 2.0 license.