---
license: mit
pretty_name: SemanticSTF
tags:
- lidar
- point-cloud
- autonomous-driving
- semantic-segmentation
- 3d-segmentation
- adverse-weather
- lidar-rgb
- cam-lidar-fusion
---

# 📌 SemanticSTF Dataset

SemanticSTF is a real multimodal LiDAR dataset collected under adverse weather conditions including **rain, snow, and fog**, for autonomous driving research.  

It provides **synchronized LiDAR point clouds, RGB images, and per-point semantic labels of 20 classes**, designed for **3D semantic segmentation and sensor fusion tasks**.

The dataset contains **train/val/test splits**, camera intrinsics/extrinsics, and high-quality annotations aligned at the frame level.

---

## 📂 Dataset Contents

The downloadable archive contains:

```
/SemanticSTF/
  └── calib/
    └── calib_cam_stereo_left.json
    └── calib_tf_tree_full.json
  └── train/
    └── train.txt
    └── velodyne
      └── 2018-02-04_11-09-42_00400.bin
      ├── 2018-02-04_11-22-09_00100.bin
      ...
    └── labels
      └── 2018-02-04_11-09-42_00400.label
      ├── 2018-02-04_11-22-09_00100.label
      ...
    └── images
      └── 2018-02-04_11-09-42_00400.png
      ├── 2018-02-04_11-22-09_00100.png
      ...
  └── val/
    └── val.txt
    └── velodyne
      ...
    └── labels
      ...
    └── images
      ...
  └── test/
    └── test.txt
    └── velodyne
      ...
    └── labels
      ...
    └── images
      ...
  ...
  └── semanticstf.yaml
```

| Modality   | Format   | Description                            |
|------------|----------|----------------------------------------|
| LiDAR      | `.bin`   | LiDAR point cloud                      |
| Labels     | `.label` | semantic label per point               |
| RGB Images | `.png`   | synchronized with LiDAR                |
| Calibration | `.json`  | camera intrinsics + LiDAR-to-camera TF |
| Weather    | `.txt`   | adverse weather per frame              |

Check [example code](https://github.com/xiaoaoran/SemanticSTF/blob/77ebee6196b6dcd9a0dce1ebe57f35c9e29c5bb7/PointDR/core/datasets/semantic_stf.py#L146) for data loading.

---
## Citation

If you find our work useful in your research, please consider citing:
```
@inproceedings{xiao20233d,
  title={3d semantic segmentation in the wild: Learning generalized models for adverse-condition point clouds},
  author={Xiao, Aoran and Huang, Jiaxing and Xuan, Weihao and Ren, Ruijie and Liu, Kangcheng and Guan, Dayan and El Saddik, Abdulmotaleb and Lu, Shijian and Xing, Eric P},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={9382--9392},
  year={2023}
}
```

SemanticSTF dataset consists of re-annotated LiDAR point cloud data from the STF dataset. Kindly consider citing it if you intend to use the data:
```
@inproceedings{bijelic2020seeing,
  title={Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather},
  author={Bijelic, Mario and Gruber, Tobias and Mannan, Fahim and Kraus, Florian and Ritter, Werner and Dietmayer, Klaus and Heide, Felix},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11682--11692},
  year={2020}
}
```