Paper Info
Title
Combining encoded structured light and photometric stereo for underwater 3D reconstruction
Abstract
Surface normal maps created by photometric stereo allow for high-quality rendering from certain viewpoints, even when the resolution of original images is low. However, the lack of constraints between multiple disconnected patches, the frequent presence of low-frequency distortion, and some actual conditions often lead to a bias during the photometric stereo reconstruction using direct integration. In this paper, we therefore present a hybrid method, which exploits the depth information that the encoded structured light system produces, in order to correct the photometric stereo bias. On the other hand, this method retains the high-precision normal information. Our experimental results show that the proposed method can not only recover high-frequency details but also avoid, or at least reduce, the low-frequency bias. In particular, the error that our method generates in the underwater environment is tolerant, even in the case that high turbidity values occur.
Year
DOI
Venue
2017
10.1109/UIC-ATC.2017.8397465
2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Keywords
Field
DocType
photometric stereo,encoded structured light,bias correction,underwater reconstruction
Iterative reconstruction,Computer vision,Structured light,Computer science,Artificial intelligence,Image restoration,Rendering (computer graphics),Distortion,Photometric stereo,Normal,3D reconstruction,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-1591-1
0
0.34
References 
Authors
10
6
Name
Order
Citations
PageRank
Xu Li14020.61
Hao Fan203.04
Lin Qi3186.47
Yijun Chen400.34
Junyu Dong59923.43
Xinghui Dong6145.00