README.md

metadata

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 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}
}