openMVG samples

openMVG focus on a strong implementation checking of the provided features. To do so it provides unit test (that assert code results and helps user to see how the code must be used) but it provides also illustrated samples of the major features.

The samples can be seen as showcase and tutorials:

imageData

  • some pictures for each of the following examples.

siftPutativeMatches

Show how:
  • extract SIFT features and descriptors,
  • match features descriptors,
  • display the computed matches.

image_describer_matches

Show how:
  • use the Image_describer interface to extract features & descriptors
  • match the detected regions
  • display detected features & corresponding matches

robust_homography

Show how:
  • estimate a robust homography between features matches.

robust_homography_guided

Show how:
  • estimate a robust homography between features matches,
  • extent the putative matches with a guided filter,
  • warp the query image over the reference image.

robust_fundamental

Show how:
  • estimate a robust fundamental matrix between features matches.

robust_essential

Show how:
  • estimate a robust essential matrix between features matches,
  • compute the 3D structure by triangulation of the corresponding inliers.

robust_essential_ba

Show how:
  • refine with bundle_adjustment the Structure and Motion of a scene
  • for different camera model:
    • Refine [X],[f,R|t] (individual cameras),
    • Refine [X],[R|t], shared [f],
    • Refine [X],[R|t], shared brown disto models.

robust_essential_spherical

Show how:
  • estimate a robust essential matrix between two spherical panorama
  • triangulate remaning inliers.

kvld_filter

Show how:
  • filter putative matches with the K-VLD filter [KVLD12].

exifParsing

Show how:
  • parse JPEG EXIF metadata

sensorWidthDatabase

Show how:
  • use the camera sensor width database

undisto_Brown

Show how:
  • undistord a picture according known Brown radial parameters.

Don’t hesitate to help to extend the list.