MapAnything

MapAnything represents a simple, end-to-end trained transformer model developed for metric 3D reconstruction. This innovative model directly regresses the factored metric 3D geometry of a scene, accommodating a wide array of input types. Users can provide images, camera calibration parameters, poses, or existing depth data to generate detailed 3D reconstructions.

A key feature of MapAnything is its versatility and efficiency. It operates as a single feed-forward model, capable of supporting more than 12 different 3D reconstruction tasks. These tasks encompass a broad spectrum of applications, including multi-image Structure-from-Motion (SfM), multi-view stereo (MVS), monocular metric depth estimation, object registration, and depth completion. This comprehensive capability makes it a valuable tool for researchers and developers working with 3D scene understanding.

The project is hosted on GitHub by Facebook Research and Carnegie Mellon University, indicating its origin in advanced academic and industrial research. Its open-source nature allows for community contributions and further development in the field of deep learning for geospatial applications and computer vision.