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--- |
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tags: |
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- model_hub_mixin |
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- pytorch_model_hub_mixin |
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- computer-vision |
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- 3d-reconstruction |
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- multi-view-stereo |
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- depth-estimation |
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- camera-pose |
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- covisibility |
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- mapanything |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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pipeline_tag: image-to-3d |
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--- |
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## Overview |
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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. |
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This is the Apache 2.0 variant of the model. Latest release on Dec 18th 2025. |
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## Quick Start |
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Please refer to our Github Repo: https://github.com/facebookresearch/map-anything |
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## Citation |
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If you find our repository useful, please consider giving it a star ⭐ and citing our paper in your work: |
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```bibtex |
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@inproceedings{keetha2026mapanything, |
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title={{MapAnything}: Universal Feed-Forward Metric 3D Reconstruction}, |
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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}, |
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booktitle={International Conference on 3D Vision (3DV)}, |
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year={2026}, |
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organization={IEEE} |
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} |
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``` |
