Open AccessReconstructing 3D point clouds in real time with look-up tables for structured light scanning along both horizontal and vertical directions
Author(s) -
Kai Liu,
Jianwen Song,
Daniel L. Lau,
Xiujuan Zheng,
Ce Zhu,
Xiaomei Yang
Publication year - 2019
Publication title -
optics letters/optics index
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.524
H-Index - 272
eISSN - 1071-2763
pISSN - 0146-9592
DOI - 10.1364/ol.44.006029
Subject(s) - epipolar geometry , point cloud , computer science , projector , computation , structured light , computer graphics (images) , optics , thread (computing) , computer vision , algorithm , physics , image (mathematics) , operating system
By scanning static, not moving, objects along both the horizontal and vertical axes instead of one, structured light illumination achieves more accurate and robust 3D surface reconstructions but with greater latency on computing 3D point clouds. If scanning is performed along only one axis, it has been reported that look-up tables, manually derived from the calibration matrices of a camera and a projector, can significantly help to speed up computation; however, it has been nearly impossible to manually derive similar look-up tables for phases scanned along two axes. In this Letter, we bridge this divide by introducing the constraint of epipolar geometry to automatically compute look-up tables and thus, significantly speed up computing 3D point clouds with only basic arithmetic operations rather than time-consuming matrix computations. Experimental results show that the proposed method, using only single-thread CPU computing, reduces process latency by an order of magnitude.