This application compute corresponding features and robustly triangulate them according the geometry of the known camera intrinsics & poses.

Algorithm of the application

Require: internal + external camera calibration
Require: image description regions (features + descriptors)
Ensure: 3D point cloud
compute image visibility
  list all the pair that share common visual content
    - camera frustum based
    - or structure visbility (SfM tracks) based
list triplets of view from pairs
 for each triplets compute 3 view tracks
   if tracks triangulable add correspondences to p
link 3 views validated matches (p) as tracks
  robustly triangulate them

Information and usage

The chain is designed to run on a sfm_data.json file and some pre-computed matches. The sfm_data file should contains: - valid view with some defined intrinsics and camera poses, - (optional existing structure).

$ openMVG_main_ComputeStructureFromKnownPoses -i Dataset/out_Reconstruction/sfm_data.json -o Dataset/out_Reconstruction/robustFitting.json

Arguments description:

Required parameters:

  • [-i|–input_file]
    • a SfM_Data file with valid intrinsics and poses and optional structure
  • [-m|–matchdir]
    • path were image descriptions were stored
  • [-o|–outdir]
    • path where the updated scene data will be stored

Optional parameters:

  • [-f|–match_file]
    • path to a matches file (pairs of the match files will be listed and used)
  • [-p|–pair_file]
    • path to a pairs file (only those pairs will be considered to compute the structure) The pair file is a list of view indexes, one pair on each line
  • [-b|–bundle_adjustment]
    • perform a bundle adjustment on the scene (OFF by default)
  • [-r|–residual_threshold]
    • maximal pixels reprojection error that will be considered for triangulations (4.0 by default)