openMVG_main_SfM --sfm_engine "INCREMENTAL"

The [ACSfM]_ SfM is an evolution of the implementation used for the paper "Adaptive Structure from Motion with a contrario model estimation" published at ACCV 2012.

The incremental pipeline is a growing reconstruction process. It starts from an initial two-view reconstruction (the seed) that is iteratively extended by adding new views and 3D points, using pose estimation and triangulation. Due to the incremental nature of the process, successive steps of non-linear refinement, like Bundle Adjustment (BA) and Levenberg-Marquardt steps, are performed to minimize the accumulated error (drift).

Algorithm of the Incremental/Sequential Structure from Motion

.. code-block:: c++

Require: internal camera calibration (possibly from EXIF data) Require: pairwise geometry consistent point correspondences Ensure: 3D point cloud Ensure: camera poses compute correspondence tracks t compute connectivity graph G (1 node per view, 1 edge when enough matches) pick an edge e in G with sufficient baseline

• robustly estimate essential matrix from images of e triangulate validated tracks, which provides an initial reconstruction contract edge e while G contains an edge do pick edge e in G that maximizes union(track(e),3D points)
  • robustly estimate pose (external orientation/resection) triangulate new tracks contract edge e perform bundle adjustment end while

Information and usage

The chain is designed to run on a sfm_data.json file and some pre-computed matches.

.. code-block:: c++

$ openMVG_main_SfM --engine INCREMENTAL -i Dataset/matches/sfm_data.json -m Dataset/matches/ -o Dataset/out_Incremental_Reconstruction/

Arguments description:

Required parameters:

• [-i|--input_file]

  • a SfM_Data file

• [-m|--match_dir]

  • path were geometric matches were stored

• [-o|--out_dir]

  • path where the output data will be stored

Optional parameters:

• [-a|--initial_pair_a NAME]

  • the filename image to use (i.e. 100_7001.JPG)

• [-b|--initial_pair_b NAME]

  • the filename image to use (i.e. 100_7002.JPG)

• [-c|--camera_model]

  • The camera model type that will be used for views with unknown intrinsic:

    • 1: Pinhole
    • 2: Pinhole radial 1
    • 3: Pinhole radial 3 (default)
    • 4: Pinhole radial 3 + tangential 2
    • 5: Pinhole fisheye

• [-f|--refine_intrinsic_config] User can control exactly which parameter will be considered as constant/variable and combine them by using the '|' operator.

  • ADJUST_ALL -> refine all existing parameters (default)

  • NONE -> intrinsic parameters are held as constant

  • ADJUST_FOCAL_LENGTH -> refine only the focal length

  • ADJUST_PRINCIPAL_POINT -> refine only the principal point position

  • ADJUST_DISTORTION -> refine only the distortion coefficient(s) (if any)

  • NOTE Options can be combined thanks to '|':

    • ADJUST_FOCAL_LENGTH|ADJUST_PRINCIPAL_POINT -> refine the focal length & the principal point position

    • ADJUST_FOCAL_LENGTH|ADJUST_DISTORTION -> refine the focal length & the distortion coefficient(s) (if any)

    • ADJUST_PRINCIPAL_POINT|ADJUST_DISTORTION -> refine the principal point position & the distortion coefficient(s) (if any)

IncrementalSfM2

INCREMENTALV2 is a more generic incremental pipeline than INCREMENTAL since it can:

• extend a scene that was started by using existing poses or an abstract scene initialization (2 view or n-views).

The main features are:

• The reconstruction initialization is done thanks to an abstract interface, SfMSceneInitializer.
• The triangulation stage considers the entire scene tracks.
• The resection stage is based on 2d-3D matching confidence.
• The reconstruction can start from existing camera poses.

This new engine (SequentialSfMReconstructionEngine) is easier to read and to customize than SequentialSfMReconstructionEngine.

• fast:

  • Since it localizes images as soon as it can, fewer Bundle Adjustment steps are observed than in SequentialSfMReconstructionEngine.

• flexible:

  • The engine can extend a partial reconstruction, you can call this engine on the results of any other SfM Engine. For example, you can run GlobalSfM (to obtain the pose of the camera triplets) and then run SequentialSfMReconstructionEngine2 to localize the remaining images.

  • You can now initialize the reconstruction with a n-view reconstruction (Stellar [2]) and provide a very stable seed for the reconstruction.

For the moment three SfMSceneInitializer are implemented:

• SfMSceneInitializer:

  • Keep the existing poses. -> extend a previous reconstruction.

• SfMSceneInitializerMaxPair:

  • Initialize a 2-view reconstruction (the relative pose with the most of inliers).

• SfMSceneInitializerStellar:

  • Initialize a stellar reconstruction (a n-view reconstruction with edge connected to a central unique pose: i.e a 5 pose stellar configuration could be defined by 4 relative pairs {{0,1}, {0,2}, {0,6}, {0,10}}.)