Structure from motion

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Structure from motion (SfM) is a range imaging technique; it refers to the process of estimating three-dimensional structures from two-dimensional image sequences which may be coupled with local motion signals. It is studied in the fields of computer vision and visual perception. In biological vision, SfM refers to the phenomenon by which humans (and other living creatures) can recover 3D structure from the projected 2D (retinal) motion field of a moving object or scene.

Obtaining 3D information from 2D images[edit]

Digital surface model of motorway interchange construction site
Real photo x SfM with texture color x SfM with simple shader. Made with Python Photogrammetry Toolbox GUI and rendered in Blender with Cycles.
Bezmiechowa airfield 3D Digital Surface Model extracted from data collected during 30min flight of Pteryx UAV

Humans perceive a lot of information about the three-dimensional structure in their environment by moving through it. When the observer moves and the objects around him move, information is obtained from images sensed over time.[1]

Finding structure from motion presents a similar problem as finding structure from stereo vision. In both instances, the correspondence between images and the reconstruction of 3D object needs to be found.

To find correspondence between images, features such as corner points (edges with gradients in multiple directions) need to be tracked from one image to the next. The feature trajectories over time are then used to reconstruct their 3D positions and the camera's motion.[2]

See also[edit]

Further reading[edit]

  • Richard Hartley and Andrew Zisserman (2003). Multiple View Geometry in Computer Vision. Cambridge University Press. ISBN 0-521-54051-8. 
  • Olivier Faugeras and Quang-Tuan Luong and Theodore Papadopoulo (2001). The Geometry of Multiple Images. MIT Press. ISBN 0-262-06220-8. 
  • Yi Ma, S. Shankar Sastry, Jana Kosecka, Stefano Soatto, Jana Kosecka (November 2003). An Invitation to 3-D Vision: From Images to Geometric Models. Interdisciplinary Applied Mathematics Series, #26. Springer-Verlag New York, LLC. ISBN 0-387-00893-4. 

References[edit]

  1. ^ Linda G. Shapiro, George C. Stockman (2001). Computer Vision. Prentice Hall. ISBN 0-13-030796-3. 
  2. ^ F. Dellaert, S. Seitz, C. Thorpe, and S. Thrun (2000). "Structure from Motion without Correspondence". IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 

External links[edit]

Structure from Motion software toolboxes[edit]

OpenSource solution

  1. FIT3D (From Images to 3D) - Matlab Toolbox by Isaac Esteban
  2. Structure from Motion toolbox for Matlab by Vincent Rabaud
  3. Matlab Functions for Multiple View Geometry by Andrew Zissermann
  4. Structure and Motion Toolkit by Phil Torr
  5. Bundler - Structure from Motion for Unordered Photo Collections by Noah Snavely
  6. Libmv - A C++ Structure from Motion library
  7. openMVG An Open Multiple View Geometry library + a SfM chain demonstrator
  8. MicMac, a SFM open-source code released by the Institut national de l'information géographique et forestière
  9. Python Photogrammetry Toolbox GUI - an open-source SFM GUI (Easy SfM and dense point cloud estimation launcher) by Pierre Moulon and Arc-Team
  10. Matlab Code for Non-Rigid Structure from Motion by Lorenzo Torresani
  11. SBA for generic bundle adjustment by Manolis Lourakis.
  12. ceres-solver for general non-linear least squares. Has features for bundle adjustment. Previously used by Google internally for google maps. Released to the public in 2012.

Software

  1. Smart3DCapture, a complete photogrammetry solution by Acute3D.
  2. 3DF Samantha - Command line structure from Motion pipeline for Windows, by 3Dflow srl. Free for non-commercial purposes.
  3. Automatic Camera Tracking System (ACTS) A structure-from-motion system for Microsoft Windows, by State Key Lab of CAD&CG, Zhejiang University.
  4. VisualSFM: A Visual Structure from Motion System, by Changchang Wu
  5. SFMToolkit a complete photogrammetry solution based on open-source software
  6. MountainsMap SEM software for Scanning Electron Microscopes. 3D is obtained by tilting the specimen + photogrammetry.
  7. Voodoo Camera Tracker, non-commerial tool for the integration of virtual and real scenes.
    Original site, archived: Laboratorium für Informationstechnologie, University of Hannover
  8. MetaIO Toolbox SfM for augmented reality on mobile devices.
  9. TacitView by 2d3 Sensing
  10. Catena Python Abstract Workflow Framework with SfM components.