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---
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}
}
``` |