Shading‐based surface recovery using subdivision‐based representation

This paper presents subdivision‐based representations for both lighting and geometry in shape‐from‐shading. A very recent shading‐based method introduced a per‐vertex overall illumination model for surface reconstruction, which has advantage of conveniently handling complicated lighting condition an...

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Main Authors: Deng, Teng, Zheng, Jianmin, Cai, Jianfei, Cham, Tat-Jen
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/138216
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1382162020-04-29T04:32:26Z Shading‐based surface recovery using subdivision‐based representation Deng, Teng Zheng, Jianmin Cai, Jianfei Cham, Tat-Jen School of Computer Science and Engineering Institute for Media Innovation (IMI) Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Computer science and engineering::Computing methodologies::Computer graphics Image-based Modelling Scene Analysis This paper presents subdivision‐based representations for both lighting and geometry in shape‐from‐shading. A very recent shading‐based method introduced a per‐vertex overall illumination model for surface reconstruction, which has advantage of conveniently handling complicated lighting condition and avoiding explicit estimation of visibility and varied albedo. However, due to its discrete nature, the per‐vertex overall illumination requires a large amount of memory and lacks intrinsic coherence. To overcome these problems, in this paper we propose to use classic subdivision to define the basic smooth lighting function and surface, and introduce additional independent variables into the subdivision to adaptively model sharp changes of illumination and geometry. Compared to previous works, the new model not only preserves the merits of the per‐vertex illumination model, but also greatly reduces the number of variables required in surface recovery and intrinsically regularizes the illumination vectors and the surface. These features make the new model very suitable for multi‐view stereo surface reconstruction under general, unknown illumination condition. Particularly, a variational surface reconstruction method built upon the subdivision representations for lighting and geometry is developed. The experiments on both synthetic and real‐world data sets have demonstrated that the proposed method can achieve memory efficiency and improve surface detail recovery. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) 2020-04-29T04:32:26Z 2020-04-29T04:32:26Z 2019 Journal Article Deng, T., Zheng, J., Cai, J., & Cham, T.-J. (2019). Shading‐based surface recovery using subdivision‐based representation. Computer Graphics Forum, 38(1), 417-428. doi:10.1111/cgf.13539 0167-7055 https://hdl.handle.net/10356/138216 10.1111/cgf.13539 2-s2.0-85053525671 1 38 417 428 en Computer Graphics Forum © 2018 The Author(s). © 2018 The Eurographics Association and John Wiley & Sons Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Engineering::Computer science and engineering::Computing methodologies::Computer graphics
Image-based Modelling
Scene Analysis
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Engineering::Computer science and engineering::Computing methodologies::Computer graphics
Image-based Modelling
Scene Analysis
Deng, Teng
Zheng, Jianmin
Cai, Jianfei
Cham, Tat-Jen
Shading‐based surface recovery using subdivision‐based representation
description This paper presents subdivision‐based representations for both lighting and geometry in shape‐from‐shading. A very recent shading‐based method introduced a per‐vertex overall illumination model for surface reconstruction, which has advantage of conveniently handling complicated lighting condition and avoiding explicit estimation of visibility and varied albedo. However, due to its discrete nature, the per‐vertex overall illumination requires a large amount of memory and lacks intrinsic coherence. To overcome these problems, in this paper we propose to use classic subdivision to define the basic smooth lighting function and surface, and introduce additional independent variables into the subdivision to adaptively model sharp changes of illumination and geometry. Compared to previous works, the new model not only preserves the merits of the per‐vertex illumination model, but also greatly reduces the number of variables required in surface recovery and intrinsically regularizes the illumination vectors and the surface. These features make the new model very suitable for multi‐view stereo surface reconstruction under general, unknown illumination condition. Particularly, a variational surface reconstruction method built upon the subdivision representations for lighting and geometry is developed. The experiments on both synthetic and real‐world data sets have demonstrated that the proposed method can achieve memory efficiency and improve surface detail recovery.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Deng, Teng
Zheng, Jianmin
Cai, Jianfei
Cham, Tat-Jen
format Article
author Deng, Teng
Zheng, Jianmin
Cai, Jianfei
Cham, Tat-Jen
author_sort Deng, Teng
title Shading‐based surface recovery using subdivision‐based representation
title_short Shading‐based surface recovery using subdivision‐based representation
title_full Shading‐based surface recovery using subdivision‐based representation
title_fullStr Shading‐based surface recovery using subdivision‐based representation
title_full_unstemmed Shading‐based surface recovery using subdivision‐based representation
title_sort shading‐based surface recovery using subdivision‐based representation
publishDate 2020
url https://hdl.handle.net/10356/138216
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