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CSIE: Coded Strip-Patterns Image Enhancement Embedded in Structured Light-Based Methods

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13843))

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Abstract

When a coded strip-patterns image (CSI) is captured in a structured light system (SLs), it often suffers from low visibility at low exposure settings. Besides degrading the visual perception of the CSI, this poor quality also significantly affects the performance of 3D model reconstruction. Most of the existing image-enhanced methods, however, focus on processing natural images but not CSI. In this paper, we propose a novel and effective CSI enhancement (CSIE) method designed for SLs. More concretely, a bidirectional perceptual consistency (BPC) criterion, including relative grayscale (RG), exposure, and texture level priors, is first introduced to ensure visual consistency before and after enhancement. Then, constrained by BPC, the optimization function estimates solutions of illumination with piecewise smoothness and reflectance with detail preservation. With well-refined solutions, CSIE results can be achieved accordingly and further improve the details performance of 3D model reconstruction. Experiments on multiple sets of challenging CSI sequences show that our CSIE outperforms the existing used for natural image-enhanced methods in terms of 2D enhancement, point clouds extraction (at least 17% improvement), and 3D model reconstruction.

W. Cao and Y. Ye—These authors contributed equally to this work.

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Acknowledgements

This work is supported by the Key-Area Research and Development Program of Guangdong Province, China (2019B010149002).

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Authors and Affiliations

  1. Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Wei Cao, Yuping Ye, Chu Shi & Zhan Song

Authors
  1. Wei Cao
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  2. Yuping Ye
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  3. Chu Shi
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  4. Zhan Song
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Corresponding author

Correspondence to Zhan Song .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Cao, W., Ye, Y., Shi, C., Song, Z. (2023). CSIE: Coded Strip-Patterns Image Enhancement Embedded in Structured Light-Based Methods. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13843. Springer, Cham. https://doi.org/10.1007/978-3-031-26313-2_12

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  • DOI: https://doi.org/10.1007/978-3-031-26313-2_12

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  • Online ISBN: 978-3-031-26313-2

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