Create README.md

README.md

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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}
+}
+```