Deep learning-based fringe modulation-enhancing method for accurate fringe projection profilometry
Posted on 2020-07-08 - 00:11
Fringe projection profilometry (i.e., FPP) has been one of the most popular 3-D measurement techniques. The phase error due to system random noise becomes non-ignorable when fringes captured by a camera have a low fringe modulation, which are inevitable for objects’ surface with un-uniform reflectivity. The phase calculated from these low-modulation fringes may have a non-ignorable phase error and generate 3-D measurement error. Traditional methods reduce the phase error with losing details of 3-D shapes or sacrificing the measurement speed. In this paper, a deep learning-based fringe modulation-enhancing method (i.e., FMEM) is proposed, that transforms low-modulation fringes into high-modulation fringes. FMEM enables to calculate the desired phase from transformed high-modulation fringes, and result in accurate 3-D FPP without sacrificing the speed. Experimental analysis verifies its effectiveness and accurateness.
CITE THIS COLLECTION
DataCite
3 Biotech
3D Printing in Medicine
3D Research
3D-Printed Materials and Systems
4OR
AAPG Bulletin
AAPS Open
AAPS PharmSciTech
Abhandlungen aus dem Mathematischen Seminar der Universität Hamburg
ABI Technik (German)
Academic Medicine
Academic Pediatrics
Academic Psychiatry
Academic Questions
Academy of Management Discoveries
Academy of Management Journal
Academy of Management Learning and Education
Academy of Management Perspectives
Academy of Management Proceedings
Academy of Management Review
Han, Jing; Yu, Haotian; Zheng, Dongliang; Fu, Jiaan; zhang, yi; Zuo, Chao (2020). Deep learning-based fringe modulation-enhancing method for accurate fringe projection profilometry. Optica Publishing Group. Collection. https://doi.org/10.6084/m9.figshare.c.5043164.v1
or
Select your citation style and then place your mouse over the citation text to select it.
Resource Link
SHARE
Usage metrics
Read the peer-reviewed publication
AUTHORS (6)
JH
Jing Han
HY
Haotian Yu
DZ
Dongliang Zheng
JF
Jiaan Fu
yz
yi zhang
CZ
Chao Zuo