Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/xinge008/Cylinder3D
Rank 1st in the leaderboard of SemanticKITTI semantic segmentation (both single-scan and multi-scan) (Nov. 2020) (CVPR2021 Oral)
https://github.com/xinge008/Cylinder3D
cvpr2021 lidar-point-cloud lidar-segmentation nuscenes panoptic-segmentation point-cloud semantic-segmentation semantickitti
Last synced: 3 months ago
JSON representation
Rank 1st in the leaderboard of SemanticKITTI semantic segmentation (both single-scan and multi-scan) (Nov. 2020) (CVPR2021 Oral)
- Host: GitHub
- URL: https://github.com/xinge008/Cylinder3D
- Owner: xinge008
- License: apache-2.0
- Created: 2020-08-02T08:33:00.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-04-05T08:18:32.000Z (almost 2 years ago)
- Last Synced: 2024-08-01T03:43:53.571Z (6 months ago)
- Topics: cvpr2021, lidar-point-cloud, lidar-segmentation, nuscenes, panoptic-segmentation, point-cloud, semantic-segmentation, semantickitti
- Language: Python
- Homepage:
- Size: 653 KB
- Stars: 835
- Watchers: 15
- Forks: 179
- Open Issues: 55
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
The source code of our work **"Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation**
## News
- **2022-06 [NEW:fire:]** **PVKD (CVPR2022)**, a lightweight Cylinder3D model with much higher performance has been released [here](https://github.com/cardwing/Codes-for-PVKD)
- Cylinder3D is accepted to CVPR 2021 as an **Oral** presentation
- Cylinder3D achieves the **1st place** in the leaderboard of SemanticKITTI **multiscan** semantic segmentation
- Cylinder3D achieves the 2nd place in the challenge of nuScenes LiDAR segmentation, with mIoU=0.779, fwIoU=0.899 and FPS=10Hz.
- **2020-12** We release the new version of Cylinder3D with nuScenes dataset support.
- **2020-11** We preliminarily release the Cylinder3D--v0.1, supporting the LiDAR semantic segmentation on SemanticKITTI and nuScenes.
- **2020-11** Our work achieves the **1st place** in the leaderboard of SemanticKITTI semantic segmentation (until CVPR2021 DDL, still rank 1st in term of Accuracy now), and based on the proposed method, we also achieve the **1st place** in the leaderboard of SemanticKITTI panoptic segmentation.
## Installation
### Requirements
- PyTorch >= 1.2
- yaml
- Cython
- [torch-scatter](https://github.com/rusty1s/pytorch_scatter)
- [nuScenes-devkit](https://github.com/nutonomy/nuscenes-devkit) (optional for nuScenes)
- [spconv](https://github.com/traveller59/spconv) (tested with spconv==1.2.1 and cuda==10.2)
## Data Preparation
### SemanticKITTI
```
./
├──
├── ...
└── 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/
└── ...
```
### nuScenes
```
./
├──
├── ...
└── path_to_data_shown_in_config/
├──v1.0-trainval
├──v1.0-test
├──samples
├──sweeps
├──maps
```
## Training
1. modify the config/semantickitti.yaml with your custom settings. We provide a sample yaml for SemanticKITTI
2. train the network by running "sh train.sh"
### Training for nuScenes
Please refer to [NUSCENES-GUIDE](./NUSCENES-GUIDE.md)
### Pretrained Models
-- We provide a pretrained model for SemanticKITTI [LINK1](https://drive.google.com/file/d/1q4u3LlQXz89LqYW3orXL5oTs_4R2eS8P/view?usp=sharing) or [LINK2](https://pan.baidu.com/s/1c0oIL2QTTcjCo9ZEtvOIvA) (access code: xqmi)
-- For nuScenes dataset, please refer to [NUSCENES-GUIDE](./NUSCENES-GUIDE.md)
## Semantic segmentation demo for a folder of lidar scans
```
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
```
## TODO List
- [x] Release pretrained model for nuScenes.
- [x] Support multiscan semantic segmentation.
- [ ] Support more models, including PolarNet, RandLA, SequeezeV3 and etc.
- [ ] Integrate LiDAR Panotic Segmentation into the codebase.
## Reference
If you find our work useful in your research, please consider citing our [paper](https://arxiv.org/pdf/2011.10033):
```
@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}
}
```
## Acknowledgments
We thanks for the opensource codebases, [PolarSeg](https://github.com/edwardzhou130/PolarSeg) and [spconv](https://github.com/traveller59/spconv)