Create README.md

README.md

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+---
+license: other
+license_name: mixed-source-licenses
+license_link: LICENSE
+pretty_name: 2D3D-MATR Preprocessed Benchmarks (7-Scenes & RGB-D Scenes v2)
+tags:
+  - image-to-point-cloud correspondences
+  - 2d-3d registration
+  - 7Scenes
+  - RGBDScenesV2
+---
+
+# 2D3D-MATR Preprocessed Datasets (7-Scenes & RGB-D Scenes v2)
+
+This repository hosts the **preprocessed image-to-point-cloud data** used by 2D3D-MATR (ICCV 2023) for training and evaluation of 2D–3D registration between RGB images and point clouds. The data is derived from two upstream datasets:
+
+- **Microsoft 7-Scenes**
+- **RGB-D Scenes v2** (University of Washington)
+
+Frames have been sampled and paired with corresponding point cloud fragments, with ground-truth 2D–3D correspondences and relative poses, following the protocol described in the 2D3D-MATR paper.
+
+> ⚠️ This repository contains **preprocessed derivatives** of the original datasets. The licensing terms of the original data still apply. Please read the License section carefully before downloading.
+
+## Dataset Structure
+
+```text
+data/Datasets/
+└── 7Scenes/
+|   ├── metadata/
+|   └── data/
+|       ├── chess/
+|       ├── fire/
+|       ├── heads/
+|       ├── office/
+|       ├── pumpkin/
+|       ├── redkitchen/
+|       └── stairs/
+└── RGBDScenesV2/
+    ├── metadata/
+    └── data/
+        ├── rgbd-scenes-v2-scene_01/
+        ├── ...
+        └── rgbd-scenes-v2-scene_14/
+```
+
+
+## Intended Use
+
+These splits are intended for **non-commercial academic research** on image-to-point-cloud registration, cross-modal feature learning, and related 3D vision tasks. The preprocessed data is provided for reproducibility of the 2D3D-MATR's evaluation protocol.
+
+## License
+
+This repository **does not relicense** the upstream data. Two distinct licenses apply, and users must comply with both:
+
+### 7-Scenes (Microsoft Research)
+
+The 7-Scenes data is distributed under the **Microsoft Research License Agreement (MSR-LA)** and is provided for **non-commercial use only**. By downloading any portion of the 7-Scenes-derived data in this repository, you accept the terms of the MSR-LA. The original license can be obtained from the [7-Scenes project page](https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/).
+
+### RGB-D Scenes v2 (University of Washington)
+
+The RGB-D Scenes v2 dataset is publicly released by the University of Washington (Lai, Bo, and Fox) on the [RGB-D Object Dataset website](https://rgbd-dataset.cs.washington.edu/dataset/). The original release page does not specify an explicit license; it is widely used in the research community for academic, non-commercial purposes with attribution. Users wishing to redistribute or use the data outside of academic research should contact the original authors for explicit permission.
+
+### Preprocessing Scripts and Metadata
+
+Any preprocessing scripts, metadata files, and split definitions authored as part of 2D3D-MATR and included in this repository are made available under the same terms as the original 2D3D-MATR release for research reproducibility. They are derivative works of the upstream datasets and inherit the upstream restrictions for the data portions.
+
+If you intend to use this data for any **commercial purpose**, you must obtain permission from the respective dataset owners (Microsoft Research for 7-Scenes; the University of Washington for RGB-D Scenes v2).
+
+## Citation
+
+If you use this preprocessed data, please cite the 2D3D-MATR paper:
+
+```bibtex
+@inproceedings{li20232d3d,
+  title={2D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Clouds},
+  author={Li, Minhao and Qin, Zheng and Gao, Zhirui and Yi, Renjiao and Zhu, Chenyang and Guo, Yulan and Xu, Kai},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
+  pages={14128--14138},
+  year={2023}
+}
+```
+
+Please **also cite the original dataset papers**:
+
+**7Scenes**
+
+```bibtex
+@inproceedings{shotton2013scene,
+  title={Scene coordinate regression forests for camera relocalization in RGB-D images},
+  author={Shotton, Jamie and Glocker, Ben and Zach, Christopher and Izadi, Shahram and Criminisi, Antonio and Fitzgibbon, Andrew},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  pages={2930--2937},
+  year={2013}
+}
+```
+
+**RGBDScenesV2**
+
+```bibtex
+@inproceedings{lai2014unsupervised,
+  title={Unsupervised feature learning for 3D scene labeling},
+  author={Lai, Kevin and Bo, Liefeng and Fox, Dieter},
+  booktitle={2014 IEEE International Conference on Robotics and Automation (ICRA)},
+  pages={3050--3057},
+  year={2014},
+  organization={IEEE}
+}
+```
+
+## Acknowledgements
+
+We thank the authors of 7-Scenes (Microsoft Research) and RGB-D Scenes v2 (University of Washington) for releasing the original data, and the authors of 2D3D-MATR for the benchmark protocol, and the preprocessing data.