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Adaptive Structure from Motion with a Contrario Model Estimation

  • Pierre Moulon
  • Pascal Monasse
  • Renaud Marlet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7727)

Abstract

Structure from Motion (SfM) algorithms take as input multi-view stereo images (along with internal calibration information) and yield a 3D point cloud and camera orientations/poses in a common 3D coordinate system. In the case of an incremental SfM pipeline, the process requires repeated model estimations based on detected feature points: homography, fundamental and essential matrices, as well as camera poses. These estimations have a crucial impact on the quality of 3D reconstruction. We propose to improve these estimations using the a contrario methodology. While SfM pipelines usually have globally-fixed thresholds for model estimation, the a contrario principle adapts thresholds to the input data and for each model estimation. Our experiments show that adaptive thresholds reach a significantly better precision. Additionally, the user is free from having to guess thresholds or to optimistically rely on default values. There are also cases where a globally-fixed threshold policy, whatever the threshold value is, cannot provide the best accuracy, contrary to an adaptive threshold policy.

Keywords

Fundamental Matrix Adaptive Threshold Bundle Adjustment Structure From Motion Visual Odometry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pierre Moulon
    • 1
    • 2
  • Pascal Monasse
    • 1
  • Renaud Marlet
    • 1
  1. 1.LIGM (UMR CNRS), Center for Visual Computing, ENPCUniversité Paris-EstMarne-la-ValléeFrance
  2. 2.Mikros Image.Levallois-PerretFrance

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