Photometric Stereo using Constrained Bivariate Regression for General Isotropic Surfaces

被引:75
作者
Ikehata, Satoshi [1 ]
Aizawa, Kiyoharu [1 ]
机构
[1] Univ Tokyo, Tokyo 1138654, Japan
来源
2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2014年
关键词
D O I
10.1109/CVPR.2014.280
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a photometric stereo method that is purely pixelwise and handles general isotropic surfaces in a stable manner. Following the recently proposed sum-of-lobes representation of the isotropic reflectance function, we constructed a constrained bivariate regression problem where the regression function is approximated by smooth, bivariate Bernstein polynomials. The unknown normal vector was separated from the unknown reflectance function by considering the inverse representation of the image formation process, and then we could accurately compute the unknown surface normals by solving a simple and efficient quadratic programming problem. Extensive evaluations that showed the state-of-the-art performance using both synthetic and real-world images were performed.
引用
收藏
页码:2187 / 2194
页数:8
相关论文
共 21 条
  • [21] Wu L., 2010, P ACCV