| | --- |
| | tags: |
| | - model_hub_mixin |
| | - pytorch_model_hub_mixin |
| | - computer-vision |
| | - 3d-reconstruction |
| | - multi-view-stereo |
| | - depth-estimation |
| | - camera-pose |
| | - covisibility |
| | - mapanything |
| | license: cc-by-nc-4.0 |
| | language: |
| | - en |
| | pipeline_tag: image-to-3d |
| | --- |
| | |
| | ## Overview |
| |
|
| | MapAnything is a simple, end-to-end trained transformer model that directly regresses the factored metric 3D geometry of a scene given various types of modalities as inputs. A single feed-forward model supports over 12 different 3D reconstruction tasks including multi-image sfm, multi-view stereo, monocular metric depth estimation, registration, depth completion and more. |
| |
|
| | This is the Apache 2.0 variant of the model. Latest release on Dec 18th 2025. |
| |
|
| | ## Quick Start |
| |
|
| | Please refer to our Github Repo: https://github.com/facebookresearch/map-anything |
| |
|
| | ## Citation |
| |
|
| | If you find our repository useful, please consider giving it a star ⭐ and citing our paper in your work: |
| |
|
| | ```bibtex |
| | @inproceedings{keetha2026mapanything, |
| | title={{MapAnything}: Universal Feed-Forward Metric 3D Reconstruction}, |
| | author={Keetha, Nikhil and M{\"u}ller, Norman and Sch{\"o}nberger, Johannes and Porzi, Lorenzo and Zhang, Yuchen and Fischer, Tobias and Knapitsch, Arno and Zauss, Duncan and Weber, Ethan and Antunes, Nelson and others}, |
| | booktitle={International Conference on 3D Vision (3DV)}, |
| | year={2026}, |
| | organization={IEEE} |
| | } |
| | ``` |
