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Proceedings Paper

Photometric stereo via random sampling and tensor robust principal component analysis
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Paper Abstract

In this paper, we propose a method for accurate 3D reconstruction based on Photometric Stereo. Instead of applying the global least square solution on the entire over-determined system, we randomly sample the images to form a set of overlapping groups and recover the surface normal for each group using the least square method. We then employ fourdimensional Tensor Robust Principal Component Analysis (TenRPCA) to obtain the accurate 3D reconstruction. Our method outperforms global least square in handling sparse noises such as shadows and specular highlights. Experiments demonstrate the reconstruction accuracy of our approach.

Paper Details

Date Published: 10 April 2018
PDF: 8 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061539 (10 April 2018); doi: 10.1117/12.2302425
Show Author Affiliations
Yakun Ju, Ocean Univ. of China (China)
Lin Qi, Ocean Univ. of China (China)
Hao Fan, Ocean Univ. of China (China)
Liang Lu, Ocean Univ. of China (China)
Junyu Dong, Ocean Univ. of China (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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