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European Conference on Computer Vision

ECCV 2022: Computer Vision – ECCV 2022 pp 552–567Cite as

Bilateral Normal Integration

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13661))

Abstract

This paper studies the discontinuity preservation problem in recovering a surface from its surface normal map. To model discontinuities, we introduce the assumption that the surface to be recovered is semi-smooth, i.e., the surface is one-sided differentiable (hence one-sided continuous) everywhere in the horizontal and vertical directions. Under the semi-smooth surface assumption, we propose a bilaterally weighted functional for discontinuity preserving normal integration. The key idea is to relatively weight the one-sided differentiability at each point’s two sides based on the definition of one-sided depth discontinuity. As a result, our method effectively preserves discontinuities and alleviates the under- or over-segmentation artifacts in the recovered surfaces compared to existing methods. Further, we unify the normal integration problem in the orthographic and perspective cases in a new way and show effective discontinuity preservation results in both cases (Source code is available at https://github.com/hoshino042/bilateral_normal_integration.).

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Notes

  1. 1.

    This requirement is stricter than jump discontinuity, which requires the one-sided limits exist but are unequal at a point’s two sides. Figure 2(b), (c), and (e) are jump discontinuity examples, but a semi-smooth surface allows only Fig. 2(b) and (c).

  2. 2.

    See experiments in the supplementary material.

  3. 3.

    The method we call robust estimator is called non-convex estimator in [25].

  4. 4.

    https://github.com/hoshino042/NormalIntegrationhttps://github.com/yqueau/normal_integration.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Number JP19H01123, and National Natural Science Foundation of China under Grant No. 62136001, 61872012.

Author information

Authors and Affiliations

  1. Osaka University, Suita, Japan

    Xu Cao, Hiroaki Santo, Fumio Okura & Yasuyuki Matsushita

  2. NERCVT, School of Computer Science, Peking University, Beijing, China

    Boxin Shi

  3. Peng Cheng Laboratory, Shenzhen, China

    Boxin Shi

Authors
  1. Xu Cao
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  2. Hiroaki Santo
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  3. Boxin Shi
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  4. Fumio Okura
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  5. Yasuyuki Matsushita
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Corresponding author

Correspondence to Xu Cao .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

1 Electronic supplementary material

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Supplementary material 1 (pdf 7952 KB)

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Cao, X., Santo, H., Shi, B., Okura, F., Matsushita, Y. (2022). Bilateral Normal Integration. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13661. Springer, Cham. https://doi.org/10.1007/978-3-031-19769-7_32

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  • DOI: https://doi.org/10.1007/978-3-031-19769-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19768-0

  • Online ISBN: 978-3-031-19769-7

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