• Journal of KIISE:Computer Systems and Theory
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• v.32 no.8
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• pp.411-417
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• 2005

We present a method to reconstruct a canal surface from a point cloud (a set of unorganized points). A canal surface is defined as a swept surface of a moving sphere with varying radii. By using the shrinking and moving least-squares methods, we reduce a point cloud to a thin curve-like point set which can be approximated to the spine curve of a canal surface. The distance between a point in the thin point cloud and a corresponding point in the original point set represents the radius of the canal surface.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1997.04a
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• pp.129-130
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• 1997

국내 및 해외에서 생산 되는 윤활유에 Cloud-point curves 및 상용성 개념을 도입하여 이러한 데이터를 측정할수 있는 장치를 기존의 Light scatering, Neutron scatering, Visible Method등이 아닌 He-Ne Laser를 이용하여 측정하는 장치이다. He-Ne Laser을 이용하여 공중합체 용액의 Cloud-point curves를 측정하였고, 측정 Cloud-point curves 데\ulcorner를 연속열역학적 상평형관계식에 적용 coex istence curves를 추산하였으며 추산치와 측정치를 비교 검토하였다. 멀지 않은 해에는 이러한 분석장치가 상용화되어 윤활유 제품의 상용성을 평가하는 장치가 될 것으로 사료된다. 따라서, 기존 윤활유 제품 및 신제품의 상용성(compatibility)을 He-Ne Laser을 이용하여 측정하고 다른 일면으로는 윤활유 제품의 고품질화 및 제품의 경쟁력을 강화하는데 커다란 영향을 미치리라 사료된다.

• Journal of the Korea Computer Graphics Society
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• v.5 no.2
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• pp.1-7
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• 1999

We present an algorithm to reconstruct a pipe surface from a set of unorganized points. A pipe surface is defined by a spine curve and a radius of a swept sphere. In this paper, by using the shrinking and moving least-squares methods, a point cloud is reduced to a thin curve-like point set that can be easily approximated with a spine curve of a pipe surface. The distance between a point in the thin point cloud and a corresponding point in the original point set represents the radius of a swept sphere of a pipe surface.

• International Journal of CAD/CAM
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• v.12 no.1
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• pp.9-19
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• 2012

Generation of optimum planar B-spline curve in terms of minimum deviation and required fairness to approximate a target shape defined by a strip-shaped unorganized 2D point cloud is studied. It is proposed to use the location of control points as variables within the geometric optimization framework of point distance minimization. An adaptive simulated annealing heuristic optimization algorithm is developed to iteratively update an initial approximate curve towards the target shape. The new implementation comprises an adaptive cooling procedure in which the temperature change is adaptively dependent on the objective function evolution. It is shown that the proposed method results in an improved convergence speed when compared to the standard simulated annealing method. A couple of examples are included to show the applicability of the proposed method in the surface model reconstruction directly from point cloud data.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.5
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• pp.127-134
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• 2007

Presented in the paper is an algorithm to generate a section curve by slicing a point cloud including tens of thousands of points. Although, there have been previous research results on the slicing problem, they are quite sensitive on the density variations of the point cloud, as well as on the local noise in the point cloud. To relive the difficulties, three technological requirements are identified; 1) dominant point sampling, 2) avoiding local vibration, and 3) robustness on the density changes. To satisfy these requirements, we propose a new slicing algorithm which is based on a node-sphere diagram. The algorithm has been implemented and tested with various examples.

• Current Optics and Photonics
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• v.8 no.2
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• pp.170-182
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• 2024

The dimensional accuracy and consistency of a dual oil circuit centrifugal fuel nozzle are important for fuel distribution and combustion efficiency in an engine combustion chamber. A point cloud measurement method was proposed to solve the geometric accuracy detection problem for the fuel nozzle. An improved variance focus measure operator was used to extract the depth point cloud. Compared with other traditional sharpness evaluation functions, the improved operator can generate the best evaluation curve, and has the least noise and the shortest calculation time. The experimental results of point cloud slicing measurement show that the best window size is 24 × 24 pixels. In the height measurement experiment of the standard sample block, the relative error is 2.32%, and in the fuel nozzle cone angle measurement experiment, the relative error is 2.46%, which can meet the high precision requirements of a dual oil circuit centrifugal fuel nozzle.

• Macromolecular Research
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• v.11 no.4
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• pp.231-235
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• 2003

Phase behavior information is necessary for accomplishing homogeneous copolymerization to obtain high yield of copolymers and prevent a fouling problem. Cloud-point data to $160^{\circ}C$ and 1,450 bar are presented for five $C_6$ hydrocarbon solvents, normal hexane, 2,2-dimethyl butane, 2,3-dimethyl butane, 2-methyl pentane, and 3-methyl pentane, with poly(ethylene-co-53 mol% norbornene) ($PEN_{53}$). The pressure-concentration isotherms measured for $PEN_{53}$/n-hexane have maximums that range between 5 and 12 wt% $PEN_{53}$. The cloud-point curves for $PEN_{53}$ all have negative slopes that decrease in pressure with temperatures. The single-phase region of $PEN_{53}$ in n-hexane is larger than the regions in 2,2-dimethyl butane, 2,3-dimethyl butane, 2-methyl pentane, and 3-methyl pentane. The cloud-point curve of $PEN_{53}$ in 2,2-dimethyl butane is located at higher temperatures and pressures than the curve in 2,3-dimethyl butane due to the reduced dispersion interactions with and limited access of 2,2-dimethyl butane to the copolymer. Similar cloud-point behavior is observed for $PEN_{53}$ in 2-methyl pentane and 3-methyl pentane.

• Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.202-212
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• 2014

To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components' accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components' accuracy by comparing each component's point cloud data scanned by laser scanners and the ship's design data formatted in CAD cannot be processed efficiently when (1) extract components from point cloud data include irregular obstacles endogenously, or when (2) registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles' shadows. The ICP (Iterative Closest Point) algorithm conducts a registration of the two sets of data after the proper registration's direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.

• Korean Journal of Computational Design and Engineering
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• v.12 no.2
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• pp.146-152
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• 2007

Presented in the paper is an algorithm to generate a section curve by slicing a point cloud which may include tens of thousands of points. Although there are couple of previous results, they are very sensitive on the density variations and local noising points. In the paper, three technological requirements are identified; 1) dominant point sampling, 2) avoiding local vibration, and 3) robustness on the density changes. To satisfy these requirements, we propose a new slicing algorithm which is based on a node-circle diagram. The algorithm has been implemented and tested with various examples.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.680-681
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• 2015

This study develops an algorithm that automatically performs reverse engineering on three-dimensional (3D) sweeping shapes using a user's pre-defined feature templates and 3D point cloud data (PCD) of sweeping shapes. Existing methods extract 3D sweeping shapes by extracting points on a PCD cross section together with the center point in order to perform curve fitting and connect the center points. However, a drawback of existing methods is the difficulty of creating a 3D sweeping shape in which the user's preferred feature center points and parameters are applied. This study extracts shape features from cross-sectional points extracted automatically from the PCD and compared with pre-defined feature templates for similarities, thereby acquiring the most similar template cross-section. Fitting the most similar template cross-section to sweeping shape modeling makes the reverse engineering process automatic.