Summary study of data-driven photometric stereo methods

被引：1

作者：

Zheng Q. ^{[1
]}

Shi B. ^{[2
]}

Pan G. ^{[3
]}

机构：

[1] School of Electrical and Electronic Engineering, Nanyang Technological University

[2] National Engineering Laboratory for Video Technology, Department of CS, Peking University, Beijing

[3] State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou

来源：

Virtual Reality and Intelligent Hardware | 2020年 / 2卷 / 03期

关键词：

Data-driven methods; Non-Lambertian reflectance; Photometric stereo;

D O I：

10.1016/j.vrih.2020.03.001

中图分类号：

学科分类号：

摘要：

Background: A photometric stereo method aims to recover the surface normal of a 3D object observed under varying light directions. It is an ill-defined problem because the general reflectance properties of the surface are unknown. Methods: This paper reviews existing data-driven methods, with a focus on their technical insights into the photometric stereo problem. We divide these methods into two categories, per-pixel and all-pixel, according to how they process an image. We discuss the differences and relationships between these methods from the perspective of inputs, networks, and data, which are key factors in designing a deep learning approach. Results: We demonstrate the performance of the models using a popular benchmark dataset. Conclusions: Data-driven photometric stereo methods have shown that they possess a superior performance advantage over traditional methods. However, these methods suffer from various limitations, such as limited generalization capability. Finally, this study suggests directions for future research. © 2019 Beijing Zhongke Journal Publishing Co. Ltd

引用

页码：213 / 221

页数：8

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