Photometric stereo via random sampling and tensor robust principal component analysis

Yakun Ju; Lin Qi; Hao Fan; Liang Lu; Junyu Dong

doi:10.1117/12.2302425

10 April 2018 Photometric stereo via random sampling and tensor robust principal component analysis

Yakun Ju, Lin Qi, Hao Fan, Liang Lu, Junyu Dong

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061539 (2018) https://doi.org/10.1117/12.2302425
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

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.

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Yakun Ju, Lin Qi, Hao Fan, Liang Lu, and Junyu Dong "Photometric stereo via random sampling and tensor robust principal component analysis", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061539 (10 April 2018); https://doi.org/10.1117/12.2302425

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KEYWORDS

Principal component analysis

Error analysis

3D modeling

Optical spheres

Reconstruction algorithms

RGB color model

Light sources

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