README.md

[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2204.12463)
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# 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](https://arxiv.org/abs/2204.12463)]** <br />
Yukang Chen, Yanwei Li, Xiangyu Zhang, Jian Sun, Jiaya Jia<br />

<p align="center"> <img src="https://github.com/dvlab-research/FocalsConv/blob/master/docs/imgs/FocalSparseConv23D.png" width="100%"> </p>

<p align="center"> <img src="https://github.com/dvlab-research/FocalsConv/blob/master/docs/imgs/FocalSparseConv_Pipeline.png" width="100%"> </p>

Visualization of voxel distribution of Focals Conv on KITTI val dataset:
<p align="center"> <img src="https://github.com/dvlab-research/FocalsConv/blob/master/docs/imgs/Sparsity_comparison_3pairs.png" width="100%"> </p>


## 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](LICENSE).