Fast 3D measurement based on improved optical flow for dynamic objects
Author(s) -
Mingyou Dai,
Kuang Peng,
Jiang Zhao,
Meilin Wan,
Wenfeng Wang,
Yiping Cao
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.394691
Subject(s) - optical flow , pixel , computer science , computation , motion vector , computer vision , matching (statistics) , traverse , optics , artificial intelligence , digital image correlation , phase correlation , flow (mathematics) , algorithm , process (computing) , mathematics , image (mathematics) , physics , geometry , fourier analysis , mathematical analysis , statistics , geodesy , short time fourier transform , fourier transform , geography , operating system
High resolution, real-time three-dimensional (3D) measurement plays an important role in many fields. In this paper, a multi-directional dynamic real-time phase measurement profilometry based on improved optical flow is proposed. In a five-step phase shifting dynamic measurement, pixel matching is needed to make the pixels one-to-one corresponding in five patterns. However, in the frequently-used pixel matching method at present, it is necessary to calculate the correlation and traverse the whole deformed pattern for the motion information of the measured object. The huge amount of computation caused by correlation computation takes up most of the time in the process of the entire 3D reconstruction, so it can not meet the requirement of real-time dynamic measurement. In order to solve the problem, the improved optical flow algorithm is introduced to replace correlation calculation in pixel matching. In one measurement, five captured patterns need to be dealt with, and the optical flow between each two adjacent frames is calculated. Then four two-dimensional vector matrices can be obtained. The vector matrices contain the complete motion information of the measured object. Experiments and simulations prove that this method can improve the efficiency of pixel matching by 42 times and 3D reconstruction by 32 times on the premise of ensuring the accuracy.
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