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--- |
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license: cc-by-4.0 |
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pretty_name: 3DCompat200 |
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size_categories: |
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- 1K<n<10K |
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--- |
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# 3DCoMPaT200 Dataset |
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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. |
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## 📁 Directory Structure |
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### 2D Folder |
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Contains train, validation, and test data in tar format for 10 compositions: |
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- Training set |
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- Validation set |
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- Test set |
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Each file contains 2D representations of the objects with their corresponding compositional part annotations. |
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### HDF5 Folder |
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Contains point cloud data in HDF5 format with 2048 points per shape: |
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- Single composition datasets (train/val/test) |
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- 10 composition datasets (train/val/test) |
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The HDF5 files are optimized for efficient loading and processing of point cloud data. |
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### Challenge Folder |
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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. |
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### Compat200.zip |
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Contains the original 3D object files in GLTF format: |
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- Training set objects |
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- Validation set objects |
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Note: Test set objects are not included in this file. |
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## 🔍 Dataset Details |
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- Number of points per shape: 2048 |
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- Number of compositions: 1 and 10 variants |
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- File formats: TAR, HDF5, GLTF |
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## 🚀 Usage Instructions |
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For detailed instructions on how to use the dataset, including code examples and utility functions, please visit our GitHub repository: |
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[https://github.com/3DCoMPaT200/3DCoMPaT200](https://github.com/3DCoMPaT200/3DCoMPaT200) |
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The repository contains loaders, rendering tools, and example code to help you get started with the dataset. |
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## 📝 Citation |
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If you use our dataset, please cite the three following references: |
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```bibtex |
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@inproceedings{ahmed2024dcompat, |
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title={3{DC}o{MP}aT200: Language Grounded Large-Scale 3D Vision Dataset for Compositional Recognition}, |
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author={Mahmoud Ahmed and Xiang Li and Arpit Prajapati and Mohamed Elhoseiny}, |
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booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, |
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year={2024}, |
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url={https://openreview.net/forum?id=L4yLhMjCOR} |
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} |
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``` |
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```bibtex |
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@article{slim2023_3dcompatplus, |
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title={3DCoMPaT++: An improved Large-scale 3D Vision Dataset |
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for Compositional Recognition}, |
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author={Habib Slim, Xiang Li, Yuchen Li, |
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Mahmoud Ahmed, Mohamed Ayman, Ujjwal Upadhyay |
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Ahmed Abdelreheem, Arpit Prajapati, |
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Suhail Pothigara, Peter Wonka, Mohamed Elhoseiny}, |
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year={2023} |
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} |
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``` |
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```bibtex |
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@article{li2022_3dcompat, |
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title={3D CoMPaT: Composition of Materials on Parts of 3D Things}, |
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author={Yuchen Li, Ujjwal Upadhyay, Habib Slim, |
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Ahmed Abdelreheem, Arpit Prajapati, |
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Suhail Pothigara, Peter Wonka, Mohamed Elhoseiny}, |
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journal = {ECCV}, |
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year={2022} |
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} |
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``` |
