Divide and conquer: high-accuracy and real-time 3D reconstruction of static objects using multiple-phase-shifted structured light illumination
Author(s) -
Kai Liu,
Wenqi Hua,
Jinghe Wei,
Jianwen Song,
Daniel L. Lau,
Ce Zhu,
Bin Xu
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.386184
Subject(s) - inverse trigonometric functions , computation , algorithm , computer science , phase (matter) , convolution (computer science) , divide and conquer algorithms , distortion (music) , deconvolution , lookup table , optics , artificial intelligence , mathematics , physics , mathematical analysis , amplifier , computer network , bandwidth (computing) , quantum mechanics , artificial neural network , programming language
Multiple-phase-shifted structured light illumination achieves high-accuracy 3D reconstructions of static objects, while typically it can't achieve real-time phase computation. In this paper, we propose to compute modulations and phases of multiple scans in real time by using divide-and-conquer solutions. First, we categorize total N = KM images into M groups and each group contains K phase equally shifted images; second, we compute the phase of each group; and finally, we obtain the final phase by averaging all the separately computed phases. When K = 3, 4 or 6, we can use integer-valued intensities of images as inputs and build one or M look-up tables storing real-valued phases computed by using arctangent function. Thus, with addition and/or subtraction operations computing indices of the tables, we can directly access the pre-computed phases and avoid time-consuming arctangent computation. Compared with K-step phase measuring profilometry repeated for M times, the proposed is robust to nonlinear distortion of structured light systems. Experiments show that, first, the proposed is of the same accuracy level as the traditional algorithm, and secondly, with employing one core of a central processing unit, compared with the classical 12-step phase measuring profilometry algorithm, for K = 4 and M = 3, the proposed improves phase computation by a factor of 6 ×.
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