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