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---
license: cc-by-4.0
pretty_name: 3DCompat200
size_categories:
- 1K<n<10K
---
# 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}
}
``` |