Redundancy elimination for overlapping point clouds based on two-dimensional corresponding point pair constraints between adjacent camera stations in a grating projection rotation measurement system

Wen Gong; Yong Liu; Chuang Wang; Jun Tang

doi:10.1364/AO.58.008295

Applied Optics
Vol. 58,
Issue 30,
pp. 8295-8301
(2019)
•https://doi.org/10.1364/AO.58.008295

Redundancy elimination for overlapping point clouds based on two-dimensional corresponding point pair constraints between adjacent camera stations in a grating projection rotation measurement system

Wen Gong, Yong Liu, Chuang Wang, and Jun Tang

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Wen Gong, Yong Liu, Chuang Wang, and Jun Tang, "Redundancy elimination for overlapping point clouds based on two-dimensional corresponding point pair constraints between adjacent camera stations in a grating projection rotation measurement system," Appl. Opt. 58, 8295-8301 (2019)

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Abstract

A grating projection shape measurement system has been a commonly used method in the field of three-dimensional (3D) reconstruction in recent years, and global point cloud registration is a key step in this method. However, in the registration process, a large amount of low-precision overlapping redundant data (ORD) is generated between adjacent camera stations, which will seriously affect the speed and accuracy of later modeling. Therefore, how to eliminate these low-precision ORD is a major problem to be solved at present. Determining all overlapping 3D point pairs between two adjacent stations and deleting the points with low precision in the point pairs is the key to solving this problem. Therefore, based on an omnidirectional rotation measurement system, combined with the constraint relationships between the projection space and the acquisition space in the global registration process and the stereo-matching method of space conversion, an elimination algorithm for ORD with a two-dimensional (2D) phase constraint and a 2D pixel constraint is proposed. The experimental results show that the proposed algorithm can faster locate overlapping 3D point pairs between adjacent stations, with a higher elimination rate, and the accuracy of the overall point cloud is higher after the redundancy elimination.

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Point Cloud	Overall	M1	Proposed Algorithm
Points Num	91,262	30,989	51,529
RER/%	0.00	0.00	43.54
APS/mm	0.2752	0.4484	0.4023

Point Cloud	Original Point Cloud		Yuan’s Algorithm		Proposed Algorithm
Point Cloud	Test1	Test2	Test1	Test2	Test1	Test2
Point Num	409,759	190,708	273,426	123,886	247,810	106,543
RER/%	0.00	0.00	33.60	35.04	39.52	44.13
$E_{max} / m m$	$- 1.0564 / + 1.0564$	$- 1.2097 / + 1.2165$	$- 1.0479 / + 1.0474$	$- 1.2132 / + 1.2201$	$- 0.9793 / + 0.9786$	$- 0.9303 / + 0.9106$
$E_{a v g} / m m$	$- 0.2927 / + 0.2777$	$- 0.1062 / + 0.1042$	$- 0.2913 / + 0.2720$	$- 0.1047 / + 0.1033$	$- 0.2744 / + 0.2548$	$- 0.0991 / + 0.0974$
$E_{s d} / m m$	0.3521	0.1416	0.3490	0.1409	0.3267	0.1284
Run Time/s	/	/	120.969	63.344	34.891	13.327

Point Cloud	Overall	M1	Proposed Algorithm
Points Num	91,262	30,989	51,529
RER/%	0.00	0.00	43.54
APS/mm	0.2752	0.4484	0.4023

Point Cloud	Original Point Cloud		Yuan’s Algorithm		Proposed Algorithm
Point Cloud	Test1	Test2	Test1	Test2	Test1	Test2
Point Num	409,759	190,708	273,426	123,886	247,810	106,543
RER/%	0.00	0.00	33.60	35.04	39.52	44.13
$E_{max} / m m$	$- 1.0564 / + 1.0564$	$- 1.2097 / + 1.2165$	$- 1.0479 / + 1.0474$	$- 1.2132 / + 1.2201$	$- 0.9793 / + 0.9786$	$- 0.9303 / + 0.9106$
$E_{a v g} / m m$	$- 0.2927 / + 0.2777$	$- 0.1062 / + 0.1042$	$- 0.2913 / + 0.2720$	$- 0.1047 / + 0.1033$	$- 0.2744 / + 0.2548$	$- 0.0991 / + 0.0974$
$E_{s d} / m m$	0.3521	0.1416	0.3490	0.1409	0.3267	0.1284
Run Time/s	/	/	120.969	63.344	34.891	13.327

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Applied Optics