| | --- |
| | license: apache-2.0 |
| | task_categories: |
| | - image-to-3d |
| | - depth-estimation |
| | language: |
| | - en |
| | tags: |
| | - computer-vision |
| | - 3d-reconstruction |
| | - multi-view-stereo |
| | - depth-estimation |
| | - camera-pose |
| | - covisibility |
| | - mapanything |
| | pretty_name: MapAnything Training Metadata Dataset |
| | size_categories: |
| | - 100B<n<1T |
| | --- |
| | |
| | # MapAnything Training Metadata Dataset |
| |
|
| | ## Dataset Description |
| |
|
| | This dataset contains pre-computed metadata and covisibility matrices for supporting the [MapAnything codebase](https://github.com/facebookresearch/map-anything). This metadata enables easy reproducible training for feed-forward 3D reconstruction tasks. |
| |
|
| | Please see our [Data Processing README](https://github.com/facebookresearch/map-anything/data_processing/README.md) for more details. |
| |
|
| | ## Citation |
| |
|
| | If you use this dataset in your research, please cite our paper: |
| |
|
| | ```bibtex |
| | @inproceedings{keetha2026mapanything, |
| | title={{MapAnything}: Universal Feed-Forward Metric {3D} Reconstruction}, |
| | author={Nikhil Keetha and Norman M\"{u}ller and Johannes Sch\"{o}nberger and Lorenzo Porzi and Yuchen Zhang and Tobias Fischer and Arno Knapitsch and Duncan Zauss and Ethan Weber and Nelson Antunes and Jonathon Luiten and Manuel Lopez-Antequera and Samuel Rota Bul\`{o} and Christian Richardt and Deva Ramanan and Sebastian Scherer and Peter Kontschieder}, |
| | booktitle={International Conference on 3D Vision (3DV)}, |
| | year={2026}, |
| | organization={IEEE} |
| | } |
| | ``` |
| |
|
| | ## License |
| |
|
| | The metadata is released under the [Apache 2.0 License](https://github.com/facebookresearch/map-anything/LICENSE). |
| |
|
| | ## Links |
| |
|
| | - 🏠 **Project Page**: [https://map-anything.github.io](https://map-anything.github.io) |
| | - 💻 **Code**: [GitHub Repository](https://github.com/facebookresearch/map-anything) |
| |
|
