Rank 1st in the leaderboard of SemanticKITTI semantic segmentation (both single-scan and multi-scan) (Nov. 2020) (CVPR2021 Oral)
The source code of our work "Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
./
├──
├── ...
└── path_to_data_shown_in_config/
├──sequences
├── 00/
│ ├── velodyne/
| | ├── 000000.bin
| | ├── 000001.bin
| | └── ...
│ └── labels/
| ├── 000000.label
| ├── 000001.label
| └── ...
├── 08/ # for validation
├── 11/ # 11-21 for testing
└── 21/
└── ...
./
├──
├── ...
└── path_to_data_shown_in_config/
├──v1.0-trainval
├──v1.0-test
├──samples
├──sweeps
├──maps
Please refer to NUSCENES-GUIDE
-- We provide a pretrained model for SemanticKITTI LINK1 or LINK2 (access code: xqmi)
-- For nuScenes dataset, please refer to NUSCENES-GUIDE
python demo_folder.py --demo-folder YOUR_FOLDER --save-folder YOUR_SAVE_FOLDER
If you want to validate with your own datasets, you need to provide labels. --demo-label-folder is optional
python demo_folder.py --demo-folder YOUR_FOLDER --save-folder YOUR_SAVE_FOLDER --demo-label-folder YOUR_LABEL_FOLDER
If you find our work useful in your research, please consider citing our paper:
@article{zhu2020cylindrical,
title={Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation},
author={Zhu, Xinge and Zhou, Hui and Wang, Tai and Hong, Fangzhou and Ma, Yuexin and Li, Wei and Li, Hongsheng and Lin, Dahua},
journal={arXiv preprint arXiv:2011.10033},
year={2020}
}
#for LiDAR panoptic segmentation
@article{hong2020lidar,
title={LiDAR-based Panoptic Segmentation via Dynamic Shifting Network},
author={Hong, Fangzhou and Zhou, Hui and Zhu, Xinge and Li, Hongsheng and Liu, Ziwei},
journal={arXiv preprint arXiv:2011.11964},
year={2020}
}