Adaptive structure from motion with a contrario model estimation

Authors:
Pierre Moulon;Pascal Monasse;Renaud Marlet
Affiliations:
LIGM (UMR CNRS), Center for Visual Computing, ENPC, Université Paris-Est, Marne-la-Vallée, France,Mikros Image., Levallois-Perret, France;LIGM (UMR CNRS), Center for Visual Computing, ENPC, Université Paris-Est, Marne-la-Vallée, France;LIGM (UMR CNRS), Center for Visual Computing, ENPC, Université Paris-Est, Marne-la-Vallée, France
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Year:
2012

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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.