A robust approach based on photometric stereo for surface reconstruction

被引：0
|
作者：
Wu, Lun [1 ]
Wang, Yong-Tian [1 ]
Liu, Yue [1 ]
机构：
[1] School of Optoelectronics, Beijing Institute of Technology
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2013年 / 39卷 / 08期
关键词：
Low-rank matrix; Photometric stereo; Robust principle component analysis (RPCA); Sparse error; Surface reconstruction;
D O I：
10.3724/SP.J.1004.2013.01339
中图分类号：
学科分类号：
摘要：
We present a new framework for surface reconstruction with technique of photometric stereo, which is based on advanced convex optimization technique. We firstly remove the errors in images by robust principle component analysis (RPCA), and then obtain low-rank matrix and surface normal field. Unlike previous approaches, this method uses all the available information to simultaneously fix missing and erroneous entries. The new technique is more computationally efficient and provides theoretical assurance for robustness to large errors. Experimental results demonstrate that this framework can improve the precision for surface reconstruction with noise. Copyright © 2013 Acta Automatica Sinica. All rights reserved.
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页码：1339 / 1348
页数：9
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