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README.md

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+# 3DCoMPaT200 Dataset
+
+The 3DCoMPaT200 dataset is a comprehensive collection of 3D objects with compositional part annotations. This repository contains various formats and versions of the dataset organized for different use cases.
+
+## 📁 Directory Structure
+
+### 2D Folder
+Contains train, validation, and test data in tar format for 10 compositions:
+- Training set
+- Validation set
+- Test set
+
+Each file contains 2D representations of the objects with their corresponding compositional part annotations.
+
+### HDF5 Folder
+Contains point cloud data in HDF5 format with 2048 points per shape:
+- Single composition datasets (train/val/test)
+- 10 composition datasets (train/val/test)
+
+The HDF5 files are optimized for efficient loading and processing of point cloud data.
+
+### Challenge Folder
+Contains grounding prompts used for the Grounded Segmentation Challenge. These prompts are designed to evaluate models' ability to perform semantic segmentation based on natural language descriptions.
+
+### Compat200.zip
+Contains the original 3D object files in GLTF format:
+- Training set objects
+- Validation set objects
+Note: Test set objects are not included in this file.
+
+## 🔍 Dataset Details
+- Number of points per shape: 2048
+- Number of compositions: 1 and 10 variants
+- File formats: TAR, HDF5, GLTF
+
+## 🚀 Usage Instructions
+For detailed instructions on how to use the dataset, including code examples and utility functions, please visit our GitHub repository:
+[https://github.com/3DCoMPaT200/3DCoMPaT200](https://github.com/3DCoMPaT200/3DCoMPaT200)
+
+The repository contains loaders, rendering tools, and example code to help you get started with the dataset.
+
+## 📝 Citation
+If you use our dataset, please cite the three following references:
+
+```bibtex
+@inproceedings{ahmed2024dcompat,
+title={3{DC}o{MP}aT200: Language Grounded Large-Scale 3D Vision Dataset for Compositional Recognition},
+author={Mahmoud Ahmed and Xiang Li and Arpit Prajapati and Mohamed Elhoseiny},
+booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
+year={2024},
+url={https://openreview.net/forum?id=L4yLhMjCOR}
+}
+```
+
+```bibtex
+@article{slim2023_3dcompatplus,
+    title={3DCoMPaT++: An improved Large-scale 3D Vision Dataset
+    for Compositional Recognition},
+    author={Habib Slim, Xiang Li, Yuchen Li,
+    Mahmoud Ahmed, Mohamed Ayman, Ujjwal Upadhyay
+    Ahmed Abdelreheem, Arpit Prajapati,
+    Suhail Pothigara, Peter Wonka, Mohamed Elhoseiny},
+    year={2023}
+}
+```
+
+```bibtex
+@article{li2022_3dcompat,
+    title={3D CoMPaT: Composition of Materials on Parts of 3D Things},
+    author={Yuchen Li, Ujjwal Upadhyay, Habib Slim,
+    Ahmed Abdelreheem, Arpit Prajapati,
+    Suhail Pothigara, Peter Wonka, Mohamed Elhoseiny},
+    journal = {ECCV},
+    year={2022}
+}
+```
+