| --- |
| 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} |
| } |
| ``` |
