3D reconstruction of structured light fields based on point cloud adaptive repair for highly reflective surfaces

Wei Feng; Tong Qu; Junhui Gao; Henghui Wang; Xiuhua Li; Zhongsheng Zhai; Daxing Zhao

doi:10.1364/AO.431538

Applied Optics
Vol. 60,
Issue 24,
pp. 7086-7093
(2021)
•https://doi.org/10.1364/AO.431538

3D reconstruction of structured light fields based on point cloud adaptive repair for highly reflective surfaces

Wei Feng, Tong Qu, Junhui Gao, Henghui Wang, Xiuhua Li, Zhongsheng Zhai, and Daxing Zhao

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Wei Feng, Tong Qu, Junhui Gao, Henghui Wang, Xiuhua Li, Zhongsheng Zhai, and Daxing Zhao, "3D reconstruction of structured light fields based on point cloud adaptive repair for highly reflective surfaces," Appl. Opt. 60, 7086-7093 (2021)

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Table of Contents Category
- Instrumentation and Measurements
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About this Article
History
- Original Manuscript: May 17, 2021
- Revised Manuscript: June 30, 2021
- Manuscript Accepted: July 6, 2021
- Published: August 12, 2021

Abstract

In this paper, a novel method, to the best of our knowledge, of structured light fields based on point cloud adaptive repair is proposed to realize 3D reconstruction for highly reflective surfaces. We have designed and built a focused light field camera whose spatial and angular resolution can be flexibly adjusted as required. Then the subaperture image extraction algorithm based on image mosaic is deduced and presented to obtain multidirectional images. After that, the 3D reconstruction of structured light field imaging based on point cloud adaptive repair is presented to accurately reconstruct for highly reflective surfaces. In addition, a method based on smoothness and repair rate is also proposed to objectively evaluate the performance of the 3D reconstruction. Experimental results demonstrate the validity of the proposed method to perform high-quality depth reconstruction for highly reflective surfaces. Generally, our method takes advantage of the multidirectional imaging of the light field camera and can ensure good modulation effect of structured light while avoiding hardware complexity, which makes it application more convenient.

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Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Input:	${P_{i} \| i \in [1, n]}$ Raw point clouds; $n$ : The number of the multidirectional point clouds;
	Obtain central point cloud $P_{c} {p (x, y, z)}$ , and remainder point clouds ${P_{i} \| i \in [1, n - 1]}$
Step 1	for each $i \in [1, n - 1]$ do
	Project $P_{c}$ to $X O Y$ plane;
	Obtain projecting image $I_{b}$ ;
	for each pixel $(u, v)$ at $I_{b}$ do
	if exit point $p$ $(x, y, 0)$ at $(u, v)$ then
	$I_{b} (u, v) = 1$ ;
	else
	$I_{b} (u, v) = 0$ ;
	Carry out connected component analysis to
$I_{b} (x, y)$ ;
	Obtain the missing regions ${M}$ ;
Step 2	Select $P_{i}$ to registered with $P_{c}$ based on ICP
	algorithm;
	Obtain fusion point cloud $P_{f}$ ;
	Extract the missing part
	${P_{f}^{'} (x_{s}, y_{s}, z_{s}) \| (x_{s}, y_{s}) \in M}$ from $P_{f}$ ;
	Splicing central point cloud $P_{c}$ with ${P_{f}^{'}}$ ;
	Obtain repaired point cloud $P_{r}$ ;
Step 3	Project $P_{r}$ to $X O Y$ plane and obtain the missing
	regions ${M_{r}}$ ;
	if the region of ${M} < V$ then
	Break the loop;
	Return repaired point cloud $P_{c}$ ;
Output:	Completely repaired point cloud.

Objects	Directions	Maximal Value (mm)	Average Error (mm)	Standard Deviation (mm)
Object A	Negative direction	−2.9850	−0.4138	0.3769
	Forward direction	1.6557	0.1563	0.2913
Object B	Negative direction	−2.5694	−0.3992	0.2661
	Forward direction	1.3342	0.1271	0.2391

Objects	Directions	Maximal Value (mm)	Average Error (mm)	Standard Deviation (mm)
Object A	Negative direction	−3.1006	−0.4260	0.3795
	Forward direction	1.6557	0.1537	0.2902
Object B	Negative direction	−2.7181	−0.4182	0.2732
	Forward direction	1.4621	0.1366	0.2453

Regions	$C_{Ω} (I_{o r i}) / Λ_{X O Y}$ (Region)	$C_{Ω} (I_{r e p}) / Λ_{X O Y}$ (Region)	Repair Rate
A	69%	98%	94%
B	65%	99%	97%
C	72%	98%	93%
D	76%	97%	88%
E	67%	97%	90%

Input:	${P_{i} \| i \in [1, n]}$ Raw point clouds; $n$ : The number of the multidirectional point clouds;
	Obtain central point cloud $P_{c} {p (x, y, z)}$ , and remainder point clouds ${P_{i} \| i \in [1, n - 1]}$
Step 1	for each $i \in [1, n - 1]$ do
	Project $P_{c}$ to $X O Y$ plane;
	Obtain projecting image $I_{b}$ ;
	for each pixel $(u, v)$ at $I_{b}$ do
	if exit point $p$ $(x, y, 0)$ at $(u, v)$ then
	$I_{b} (u, v) = 1$ ;
	else
	$I_{b} (u, v) = 0$ ;
	Carry out connected component analysis to
$I_{b} (x, y)$ ;
	Obtain the missing regions ${M}$ ;
Step 2	Select $P_{i}$ to registered with $P_{c}$ based on ICP
	algorithm;
	Obtain fusion point cloud $P_{f}$ ;
	Extract the missing part
	${P_{f}^{'} (x_{s}, y_{s}, z_{s}) \| (x_{s}, y_{s}) \in M}$ from $P_{f}$ ;
	Splicing central point cloud $P_{c}$ with ${P_{f}^{'}}$ ;
	Obtain repaired point cloud $P_{r}$ ;
Step 3	Project $P_{r}$ to $X O Y$ plane and obtain the missing
	regions ${M_{r}}$ ;
	if the region of ${M} < V$ then
	Break the loop;
	Return repaired point cloud $P_{c}$ ;
Output:	Completely repaired point cloud.

Abstract

Data Availability

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Figures (11)

Tables (4)

Equations (10)

Applied Optics