openMVG_main_ComputeMatches

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openMVG_main_ComputeMatches

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This binary compute images that have a visual overlap. Using image descriptions computed by openMVG_main_ComputeFeatures, we establish the corresponding putative photometric matches and filter the resulting correspondences using some robust geometric filters.

.. code-block:: c++

$ openMVG_main_ComputeMatches -i [..\matches\sfm_data.json] -o [...\matches]

Arguments description:

Required parameters:

• [-i|--input_file]

  • a SfM_Data file

• [-o|--out_dir path]

  • path were putative and geometric matches will be stored

Optional parameters:

• [-f|--force: Force to recompute data]

  • 0: (default) reload previously computed data (useful when you have kill the process and want to continue to compute)
  • 1: useful when you change have changed a command line parameter, force recomputing and re-saving.

• [-r|-ratio]

  • (Nearest Neighbor distance ratio, default value is set to 0.8). Using 0.6 is more restrictive => provides less false positive.

• [-g|-geometric_model]

  • type of model used for robust estimation from the photometric putative matches

    • f: Fundamental matrix filtering
    • e: Essential matrix filtering
    • h: Homography matrix filtering

• [-n|--nearest_matching_method]

  • AUTO: auto choice from regions type,

  • For Scalar based descriptor you can use:

    • BRUTEFORCEL2: BruteForce L2 matching for Scalar based region descriptors,
    • ANNL2: Approximate Nearest Neighbor L2 matching for Scalar based region descriptors,
    • HNSWL2: Approximate Nearest Neighbor using L2 metric for Scalar based region descriptors,
    • HNSWL1: Approximate Nearest Neighbor using L1 metric for quantized (as unsigned char) region descriptors,
    • CASCADEHASHINGL2: L2 Cascade Hashing matching,
    • FASTCASCADEHASHINGL2: (default). L2 Cascade Hashing with precomputed hashed regions, (faster than CASCADEHASHINGL2 but use more memory).

  • For Binary based descriptor you should use:

    • BRUTEFORCEHAMMING: BruteForce Hamming matching for binary based region descriptors,
    • HNSWL1: Approximate Nearest Neighbor using Hamming distance for binary based region descriptors,

• [-v|--video_mode_matching]

  • (sequence matching with an overlap of X images)

    • X: with match 0 with (1->X), ...]
    • 2: will match 0 with (1,2), 1 with (2,3), ...
    • 3: will match 0 with (1,2,3), 1 with (2,3,4), ...]

• [-l|--pair_list]

  • file that explicitly list the View pair that must be compared

Once matches have been computed you can, at your choice, you can display detected, matches as SVG files:

• Detected keypoints: openMVG_main_exportKeypoints
• Putative, Geometric matches: openMVG_main_exportMatches
• Tracks: openMVG_main_exportTracks

Or start the 3D reconstruction:

.. toctree:: :maxdepth: 1

./IncrementalSfM.rst ./GlobalSfM.rst