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L-DPSNet: Deep Photometric Stereo Network via Local Diffuse Reflectance Maxima

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Neural Information Processing (ICONIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1516))

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Abstract

Reconstructing surface normal from the reflectance observations of real objects is a challenging issue. Although recent works on photometric stereo exploit various reflectance-normal mapping models, none of them take both illumination and LDR maximum into account. In this paper, we combine a fusion learning network with LDR maxima to recover the normal of the underlying surface. Unlike traditional formalization, the initial normal estimated by solving the generalized bas-relief (GBR) ambiguity is employed to promote the performance of our learning framework. As an uncalibrated photometric stereo network, our method, called L-DPSNet, takes advantage of LDR-derived information in normal prediction. We present the qualitative and quantitative experiments implemented using synthetic and real data to demonstrate the effectiveness of the proposed model.

Supported by the National Key R&D Program of China under Grant No. 2018YFB1701700.

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Acknowledgments

This work was supported by the National Key R&D Program of China under Grant No. 2018YFB1701700.

Author information

Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Kanghui Zeng, Chao Xu, Jing Hu & Zhaopeng Meng

  2. The Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Chao Xu & Yushi Li

  3. Higher Research Institute (Shenzhen), University of Electronic Science and Technology of China, Shenzhen, 518063, China

    Chao Xu & Jing Hu

  4. Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China

    Yushi Li & Zhaopeng Meng

Authors
  1. Kanghui Zeng
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  2. Chao Xu
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  3. Jing Hu
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  4. Yushi Li
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  5. Zhaopeng Meng
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Corresponding author

Correspondence to Jing Hu .

Editor information

Editors and Affiliations

  1. Sampoerna University, Jakarta, Indonesia

    Teddy Mantoro

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  3. Sampoerna University, Jakarta, Indonesia

    Media Anugerah Ayu

  4. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  5. Universitas Indonesia, Depok, Indonesia

    Achmad Nizar Hidayanto

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Zeng, K., Xu, C., Hu, J., Li, Y., Meng, Z. (2021). L-DPSNet: Deep Photometric Stereo Network via Local Diffuse Reflectance Maxima. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-92307-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92306-8

  • Online ISBN: 978-3-030-92307-5

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