• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.141-142
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• 2008

본 논문은 텍스처된 3차원 점군데이터를 효율적으로 모델링하는 방법을 제안한다. 지상라이다로부터 획득한 3차원 점군데이터는 많은 노이즈를 가지고 있으며 이로 인해 자동적인 모델링이 어렵다. 3차원 모델링에 있어서 메쉬를 생성해야 3차원 랜더링이 가능하지만 3차원 메쉬 생성은 노이즈에 취약하기 때문에 디자이너들이 수작업으로 노이즈를 제거해야만 한다. 하지만 노이즈 자제가 지상 라이다로부터 들어온 데이터이기 때문에 자동적인 노이즈 제거가 어렵다. 본 논문에서는 텍스처된 지상 라이다 데이터로부터 칼라 영상의 정보를 이용한 윤곽선 정보 검출 방법을 제안한다. 대부분의 건물과 같은 구조물에서 최 외곽은 같은 색의 정보를 가지고 있다. 최 외곽 칼라의 정보를 이용하여 칼라 정보의 변화를 제한하고, 유사 칼라 정보를 가지고 있는 픽셀만 얻어냄으로써 최외각 정보를 얻어낸다. 칼라 이미지를 이용만 필터링 된 점군데이터는 xy, xz, yz 각각의 평면에서 윤곽선 데이터를 가지며 이는 구조물에 대한 모델링의 속도를 빠르게 해준다.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.77-86
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• 2015

It is difficult to extract small valve automatically in noisy 3D point cloud obtained from LIDAR because small object is affected by noise considerably. In this paper, we assume that the valve is a complex model consisting of torus, cylinder and plane represents handle, rib and center plane to extract a pose of the valve. And to extract the pose, we received additional input: center of the valve. We generated histogram of distance between the center and each points of point cloud, and obtain pose of valve by extracting parameters of handle, rib and center plane. Finally, the valve is reconstructed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.143-151
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• 2009

In this paper, a new extraction algorithm for artificial structure in 3D cloud points of terrestrial LIDAR is described, considering that various obstacles in terrestrial LIDAR make it difficult to apply conventional algorithms which are designed for air-born LIDAR data. Firstly we use the R, G, B color information from the terrestrial LIDAR data to discriminate among the massive 3D cloud points. Hough transform is then applied to estimate the straight lines that correspond to the target structure. Finally, the structure is extracted by comparing the distance between the estimated line and 3D cloud points. The proposed algorithm is efficient in the sense that it requires the user interaction only when the reference colors are obtained. Computer simulation shows the performance to be quite satisfactory.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.495-507
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• 2023

The roughness of discontinuity significantly influences the mechanical characteristics of rock masses and extensively affects thermal and hydraulic behaviors. In this study, we utilized photogrammetry to generate 3D point cloud data for discontinuity and applied this data to characterize the roughness of discontinuity. The discontinuity profiles, reconstructed from the 3D point cloud data, were compared with those manually measured using a profile gauge. This comparison served to validate the accuracy and reliability of the acquired point cloud data in replicating the actual configurations of rock surfaces. Subsequent to this validation, influence of the number of profiles for representative JRC assessment was further investigated followed by suggestion of roughness anisotropy evaluation method with application of it to actual rock discontinuity surfaces.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2443-2450
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• 2014

In the motion recognition system using depth image, the depth image is converted to the real world formed 3D point cloud data for efficient algorithm apply. And then, output depth image is converted by the projective world after algorithm apply. However, when coordinate conversion, rounding error and data loss by applied algorithm are occurred. In this paper, when convert 3D point cloud data to depth image, we proposed efficient conversion method and its hardware implementation without rounding error and data loss according image size change. The proposed system make progress using the OpenCV and the window program, and we test a system using the Kinect in real time. In addition, designed using Verilog-HDL and verified through the Zynq-7000 FPGA Board of Xilinx.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.99-105
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• 2013

In rapidly developing urban areas that include high-rise, large, and complex buildings, indoor and outdoor maps in GIS become a basis for utilizing and sharing information pertaining to various aspects of the real world. Although an indoor mapping has gained much attentions, research efforts are mostly in 2D and 3D modeling of terrain and buildings. Therefore, to facilitate fast and accurate construction of indoor GIS, this paper proposes a semi-automatic method consisting of preprocessing, 2D mapping, and 3D mapping stages. The preprocessing is designed to estimate heights of building interiors and to identify noise data from point clouds. In the 2D mapping, a floor map is extracted with a tracing grid and a refinement method. In the 3D mapping, a 3D wireframe model is created with heights from the preprocessing stage. 3D mesh data converted from noise data is combined with the 3D wireframe model for detail modeling. The proposed method was applied to point clouds depicting a hallway in a building. Experiment results indicate that the proposed method can be utilized to construct 2D and 3D maps for indoor GIS.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.229-240
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• 2019

3D geo-spatial model have been widely used in the field of Civil Engineering, Medical, Computer Graphics, Urban Management and many other. Especially, the demand for high quality 3D spatial information such as precise road map construction has explosively increased, MMS and UAV techniques have been actively used to acquire them more easily and conveniently in surveying and geo-spatial field. However, in order to perform 3D modeling by integrating the two data set from MMS and UAV, its so needed an proper registration method is required to efficiently correct the difference between the raw data acquisition sensor, the point cloud data generation method, and the observation accuracy occurred when the two techniques are applied. In this study, we obtained UAV point colud data in Yeouido area as the study area in order to determine the automatic registration performance between MMS and UAV point cloud data using ICP(Iterative Closet Point) method. MMS observations was then performed in the study area by dividing 4 zones according to the level of overlap ratio and observation noise with based on UAV data. After we manually registered the MMS data to the UAV data, then compared the results which automatic registered using ICP method. In conclusion, the higher the overlap ratio and the lower the noise level, can bring the more accurate results in the automatic registration using ICP method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.35-41
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• 2020

3D laser scanners are an effective way to quickly acquire a large amount of data about an object. Recently, it is used in various fields such as surveying, displacement measurement, 3D data generation of objects, construction of indoor spatial information, and BIM(Building Information Model). In order to utilize the point cloud data acquired through the 3D laser scanner, it is necessary to make the data acquired from many stations through a matching process into one data with a unified coordinate system. However, analytical researches on the accuracy of point cloud data according to the registration method are insufficient. In this study, we tried to analyze the accuracy of registration method of point cloud data acquired through 3D laser scanner. The point cloud data of the study area was acquired by 3D laser scanner, the point cloud data was registered by the ICP(Iterative Closest Point) method and the shape registration method through the data processing, and the accuracy was analyzed by comparing with the total station survey results. As a result of the accuracy evaluation, the ICP and the shape registration method showed 0.002m~0.005m and 0.002m~0.009m difference with the total station performance, respectively, and each registration method showed a deviation of less than 0.01m. Each registration method showed less than 0.01m of variation in the experimental results, which satisfies the 1: 1,000 digital accuracy and it is suggested that the registration of point cloud data using ICP and shape matching can be utilized for constructing spatial information. In the future, matching of point cloud data by shape registration method will contribute to productivity improvement by reducing target installation in the process of building spatial information using 3D laser scanner.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.2
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• pp.179-186
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• 2004

This study describes that the method of precision measurement of vehicle shape and the method of measurement the deformation that it is occurred the reason of accident using industrial photogrammatry. The curved shape is measured using the projection target which is able to acquire the point cloud data. 3D coordinates of the target were able to acquire through object picturing and analysis of coordinates. The acquired point cloud data was done 3D modeling to form the surface with TIN. Also, It able to interpretate a deformation surveying accurately the occurred parts of deformation, then can furnish to the analysis of traffic accident the precise and effective data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.695-696
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• 2021

최근 다양한 연령층과 직업군들 사이에서 기능성 신발에 대한 관심이 증대되고 있다. 그러나 기능성 신발 및 맞춤형 신발은 높은 가격대와 긴 제작 시간이 필요하다. 이러한 문제점은 3D 스캐너 도입으로 해결이 가능하나, 정확한 발 형상 측정이 가능한 3D 스캐너는 고가의 장비이기 때문에 매장별 보급이 어렵다. 본 논문은 기능성 신발의 보급을 위하여 저가형 3D 스캐너에서 정확한 발 고유 변인을 측정할 수 있는 시스템에 대해 서술한다. 이를 위해 이를 위해 저가형 Depth Camera를 이용한 저가형 3D 스캐너의 발 형상 3D 점군 데이터를 2차원으로 변형하고, 발 형태를 감싸는 최소 사각형(Min Area Rect)를 형성하여 발 안쪽점 및 발 가쪽점을 추정한다. 생성된 최소 사각형과 발 안쪽점 및 발 가쪽점 등은 발 고유 변인 측정의 기준이 된다. 실험 결과에서는 측정 기준을 이용하여 발 고유 변인인 발 길이, 발 너비, 발꿈치 너비, 발꿈치에서 발안쪽점 및 발 가쪽점 길이 등 5가지 고유 변인을 측정하는 것을 보여준다.