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Learning Feature Subspaces for Appearance-Based Bundle Adjustment

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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

We present an improved bundle adjustment method based on the online learned appearance subspaces of 3D points. Our method incorporates the additional information from the learned appearance models into bundle adjustment. Through the online learning of the appearance models, we are able to include more plausible observations of 2D features across diverse viewpoints. Bundle adjustment can benefit from such an increase in the number of observations. Our formulation uses the appearance information to impose additional constraints on the optimization. The detailed experiments with ground-truth data show that the proposed method is able to enhance the reliability of 2D correspondences, and more important, can improve the accuracy of camera motion estimation and the overall quality of 3D reconstruction.

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Author information

Authors and Affiliations

  1. MediaTek Inc., Taiwan

    Chia-Ming Cheng

  2. National Tsing Hua University, Taiwan

    Hwann-Tzong Chen

Authors
  1. Chia-Ming Cheng
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  2. Hwann-Tzong Chen
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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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© 2013 Springer-Verlag Berlin Heidelberg

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Cheng, CM., Chen, HT. (2013). Learning Feature Subspaces for Appearance-Based Bundle Adjustment. 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_4

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  • DOI: https://doi.org/10.1007/978-3-642-37447-0_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37446-3

  • Online ISBN: 978-3-642-37447-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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