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Neural Geometric Parser for Single Image Camera Calibration

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12357))

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Abstract

We propose a neural geometric parser learning single image camera calibration for man-made scenes. Unlike previous neural approaches that rely only on semantic cues obtained from neural networks, our approach considers both semantic and geometric cues, resulting in significant accuracy improvement. The proposed framework consists of two networks. Using line segments of an image as geometric cues, the first network estimates the zenith vanishing point and generates several candidates consisting of the camera rotation and focal length. The second network evaluates each candidate based on the given image and the geometric cues, where prior knowledge of man-made scenes is used for the evaluation. With the supervision of datasets consisting of the horizontal line and focal length of the images, our networks can be trained to estimate the same camera parameters. Based on the Manhattan world assumption, we can further estimate the camera rotation and focal length in a weakly supervised manner. The experimental results reveal that the performance of our neural approach is significantly higher than that of existing state-of-the-art camera calibration techniques for single images of indoor and outdoor scenes.

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Acknowledgements

This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03034907).

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Authors and Affiliations

  1. Kookmin University, Seoul, South Korea

    Jinwoo Lee & Junho Kim

  2. Adobe Research, San Jose, USA

    Minhyuk Sung

  3. Intel Korea, Seoul, South Korea

    Hyunjoon Lee

Authors
  1. Jinwoo Lee
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  2. Minhyuk Sung
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  3. Hyunjoon Lee
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  4. Junho Kim
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Corresponding author

Correspondence to Junho Kim .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Lee, J., Sung, M., Lee, H., Kim, J. (2020). Neural Geometric Parser for Single Image Camera Calibration. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12357. Springer, Cham. https://doi.org/10.1007/978-3-030-58610-2_32

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  • DOI: https://doi.org/10.1007/978-3-030-58610-2_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58609-6

  • Online ISBN: 978-3-030-58610-2

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