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.
|Number of pages||4|
|State||Published - Dec 15 2019|
Bibliographical noteFunding Information:
Funding. National Natural Science Foundation of China (61473198); Sichuan Province Science and Technology Support Program (2018GZ0198); Chengdu Science and Technology Bureau (2018-YFYF-00029-GX); Intel Corporation; National Science Foundation (1539157).
National Natural Science Foundation of China (61473198); Sichuan Province Science and Technology Support Program (2018GZ0198); Chengdu Science and Technology Bureau (2018-YFYF-00029-GX); Intel Corporation; NationalScienceFoundation(1539157).
© 2019 Optical Society of America.
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics