| 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 | |
| - benchmarking | |
| pretty_name: MapAnything Benchmarking Dataset | |
| size_categories: | |
| - 100B<n<1T | |
| # MapAnything Benchmarking Dataset | |
| ## Dataset Description | |
| This dataset contains the WAI format data used for benchmarking feed-forward 3D reconstruction models in the [MapAnything codebase](https://github.com/facebookresearch/map-anything). | |
| 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 data 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) |