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Adaptive Structure from Motion with a Contrario Model Estimation

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Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7727))

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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.

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Author information

Authors and Affiliations

  1. LIGM (UMR CNRS), Center for Visual Computing, ENPC, Université Paris-Est, 6-8 av. Blaise Pascal, 77455, Marne-la-Vallée, France

    Pierre Moulon, Pascal Monasse & Renaud Marlet

  2. Mikros Image., 120 rue Danton, 92300, Levallois-Perret, France

    Pierre Moulon

Authors
  1. Pierre Moulon
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  2. Pascal Monasse
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  3. Renaud Marlet
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, 151-744, Seoul, Korea

    Kyoung Mu Lee

  2. Microsoft Research Asia, No. 5, Danling st., Haidian district, 100080, Beijing, P.R. China

    Yasuyuki Matsushita

  3. School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, 30332, Atlanta, GA, USA

    James M. Rehg

  4. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District,, 100 190, Beijing, P.R. China

    Zhanyi Hu

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Moulon, P., Monasse, P., Marlet, R. (2013). Adaptive Structure from Motion with a Contrario Model Estimation. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7727. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37447-0_20

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  • DOI: https://doi.org/10.1007/978-3-642-37447-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37446-3

  • Online ISBN: 978-3-642-37447-0

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