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End of preview. Expand in Data Studio
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
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:
@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}
}
@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}
}
@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}
}
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