New optical sensing system for obtaining the three‐dimensional shape of specular objects

Ryu, Young Kee

doi:10.1117/1.600685

Cited by 19 publications

(7 citation statements)

References 17 publications

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“…it is independent of the direction of view. Conversely, reflected light in case of specular surfaces significantly depends on the (Ryu and Cho, 1996;Sanderson et al, 1988). Nayar et al (1989) decomposed surface reflectance primarily into three reflection components: diffuse lobe, specular lobe and specular spike, as shown in Figure 3.…”

Section: Surface Reflectancementioning

confidence: 99%

“…A comprehensive review of reconstruction methods for specular surfaces can be found in the work by Ihrke et al (2010). The concept of single camera viewpoint and reflection of planar target at two different positions (Bonfort et al, 2006), shape from polarization technique (Morel et al, 2006), deflectometry technique (Chambard and Chalvidan, 2009), optical sensing system using parabolic mirror (Ryu and Cho, 1996), represent some of the earlier efforts to recover the shape of specular surfaces. Additionally, Maki, 2005 also used the concept of geotensity (geometrically corresponding pixel intensity) for 3D surface reconstruction of moving a object in the presence of specular reflection.…”

Section: Introductionmentioning

confidence: 99%

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The influence of surface topology on the quality of the point cloud data acquired with laser line scanning probe

2014

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Purpose -The purpose of this paper is to investigate the influence of surface topology on the performance of laser line scanning probe and to suggest methodology for 3D digitization of specular surfaces. Design/methodology/approach -Two different molds, one having milled surface and the other with polished surface, were used to identify effect of surface characteristics on the performance of laser line scanning probe mounted on bridge-type coordinate measuring machine. The point cloud data acquisition of two surfaces was carried out using different combinations of laser scanning parameters. The point cloud sets thus obtained were analyzed in terms of completeness, noise and accuracy. The polished mold which exhibited specular reflection was digitized at different scanning angles of laser line scanning probe using the best combination of scanning parameters. Findings -Results confirmed that surface characteristics play important role to determine quality of the reverse engineering (RE) process. The results in terms of completeness, accuracy and noise for point cloud sets have successfully been obtained for milled and polished surfaces. Three-dimensional (3D) comparison analysis suggested larger deviation in cases of polished surface as compared to milled surface. The point cloud set acquired with proposed approach was better in terms of both completeness and noise reduction. Originality/value -There has been an increased demand for measurement of metallic, polished and shiny surfaces in automotive, aerospace and medical industries. These surfaces are very difficult to scan because they exhibit specular reflection instead of diffuse reflection. Laser line scanning probe which is a non-contact method is in great demand for RE. This is due to the fact that it possesses very high data acquisition speed. However, laser scanning is hugely affected by surface characteristics which in turn govern specular reflection. In this paper, it has been shown that a surface that exhibits various degrees of specular reflection can be digitized efficiently if appropriate combination of scanning parameters and positions of laser line scanning probe are used. Also, this paper has attempted to offer a procedure to overcome incompleteness and noise in 3D data as obtained by the laser line scanning probe.

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Section: Surface Reflectancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The influence of surface topology on the quality of the point cloud data acquired with laser line scanning probe

2014

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Section: Measurement Of An F-theta Lens Surfacementioning

confidence: 99%

“…However, there was still residual error in the dataset which may affect the final result. The outliers may be caused by the characteristics of the measured surface such as specular reflection of the reflectance light [27] which is considered to influence the optical sensor. After the outliers were removed, the raw data were processed with Gaussian process modelling and the mean surface was obtained together with the associated uncertainty.…”

Section: Measurement Of An F-theta Lens Surfacementioning

confidence: 99%

A Gaussian Process Data Modelling and Maximum Likelihood Data Fusion Method for Multi-Sensor CMM Measurement of Freeform Surfaces

et al. 2016

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Nowadays, the use of freeform surfaces in various functional applications has become more widespread. Multi-sensor coordinate measuring machines (CMMs) are becoming popular and are produced by many CMM manufacturers since their measurement ability can be significantly improved with the help of different kinds of sensors. Moreover, the measurement accuracy after data fusion for multiple sensors can be improved. However, the improvement is affected by many issues in practice, especially when the measurement results have bias and there exists uncertainty regarding the data modelling method. This paper proposes a generic data modelling and data fusion method for the measurement of freeform surfaces using multi-sensor CMMs and attempts to study the factors which affect the fusion result. Based on the data modelling method for the original measurement datasets and the statistical Bayesian inference data fusion method, this paper presents a Gaussian process data modelling and maximum likelihood data fusion method for supporting multi-sensor CMM measurement of freeform surfaces. The datasets from different sensors are firstly modelled with the Gaussian process to obtain the mean surfaces and covariance surfaces, which represent the underlying surfaces and associated measurement uncertainties. Hence, the mean surfaces and the covariance surfaces are fused together with the maximum likelihood principle so as to obtain the statistically best estimated underlying surface and associated measurement uncertainty. With this fusion method, the overall measurement uncertainty after fusion is smaller than each of the single-sensor measurements. The capability of the proposed method is demonstrated through a series of simulations and real measurements of freeform surfaces on a multi-sensor CMM. The accuracy of the Gaussian process data modelling and the influence of the form error and measurement noise are also discussed and demonstrated in a series of experiments. The limitations and some special cases are also discussed, which should be carefully considered in practice.

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“…As a promising alternative, deflectometry enables overcoming the shortcomings in the foregoing techniques, and various laser scanning devices, based on its principle, have been fabricated [17][18][19][20][21] , but the point scanning implementations with these devices hinder them from measuring very large surfaces. For removing this limitation, the diffusive screen of a planar monitor (or a retroreflective screen coupled with a projector) is employed as structured light source so that the whole-field information can be obtained [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] .…”

Section: Introductionmentioning

confidence: 99%

Specular surface measurement by using a moving diffusive structured light source

Guo

Tao

2007

SPIE Proceedings

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This paper presents a novel technique for measuring the three-dimensional shapes of specular surfaces. Differing from the conventional techniques, the diffusive light source in our technique can be moved vertically to two different positions, and at each position the phase distribution of the deformed fringe pattern is measured, so that the orientation of incident light for each pixel is tracked through the corresponding phases. The 3-D coordinates of points on the specular surface are further determined. In so doing, the restrictions and limitations of the existing techniques in computational complexities, phase ambiguities and error accumulations are eliminated. The validity of this technique is demonstrated by experimental results.

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New optical sensing system for obtaining the three‐dimensional shape of specular objects

Cited by 19 publications

References 17 publications

The influence of surface topology on the quality of the point cloud data acquired with laser line scanning probe

The influence of surface topology on the quality of the point cloud data acquired with laser line scanning probe

A Gaussian Process Data Modelling and Maximum Likelihood Data Fusion Method for Multi-Sensor CMM Measurement of Freeform Surfaces

Specular surface measurement by using a moving diffusive structured light source

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