Update README.md

README.md

@@ -2,9 +2,39 @@
 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
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+## 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}
+}
+```