• Journal of Digital Convergence
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• v.14 no.11
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• pp.319-324
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• 2016

In the case of robot and Argument Reality applications using a camera in environments, a technology to estimate planes is a very important technology. A RGB-D camera can get a three-dimensional measurement data even in a flat which has no information of the texture of the plane;, however, there is an enormous amount of computation in order to process the point-cloud data of the image. Furthermore, it could not know the number of planes that are currently observed as an advance, also, there is an additional operation required to estimate a three dimensional plane. In this paper, we proposed the real-time method that decides the number of planes automatically and estimates the three dimensional plane by using the continuous data of an RGB-D camera. As experimental results, the proposed method showed an improvement of approximately 22 times faster speed compared to processing the entire data.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1497-1498
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• 2015

원격 로봇을 위해 지역 환경을 모델링하기 위하여, 3차원 영상을 생성하는 기술을 제안한다. 이를 위하여, 카메라와 거리 센서를 보정하는 방법에 대해서 제안한다. 그리고 카메라 영상과 거리 정보를 융합하여 점군 데이터를 생성하는 방법에 대해서 기술한다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.9
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• pp.1029-1036
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• 2014

Several types of automatic 3D scanners are available for use in the 3D scanning industry, e.g., an automatic 3D scanner that uses a robot arm and one that uses an automatic rotary table. Specifically, these scanners are used to obtain a 3D shape using automatic assisting devices. Most of these scanners are required to perform numerous operations, such as merging, aligning, trimming, and filling holes. We are interested in developing an automatic 3D shape collection device using a spherical-coordinate-based guiding system. Then, the aim of the present study is to design an automatic guiding system that can automatically collect 3D shape data. We develop a 3D model of this system and measuring data which are collected by a personal computer. An optimal design of this system and the geometrical accuracy of the measured data are both evaluated using 3D modeling software. The developed system is then applied to an object having a highly complex shape and manifold sections. Our simulation results demonstrate that the developed system collects higher-quality 3D data than the conventional method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.744-749
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• 2018

Informatization and visualization technology for real space is a key technology for construction of geospatial information. Three-dimensional (3D) modeling is a method of constructing geospatial information from data measured by various methods. The 3D laser scanner has been mainly used as a method for acquiring digital elevation data. On the other hand, the unmanned aerial vehicle (UAV), which has been attracting attention as a promising technology of the fourth industrial revolution, has been evaluated as a technology for obtaining fast geospatial information, and various studies are being carried out. However, there is a lack of evaluation on the quantitative work efficiency and data accuracy of the data construction technology for 3D geospatial modeling. In this study, various analyses were carried out on the characteristics, work processes, and accuracy of point cloud data acquired by a 3D laser scanner and an unmanned aerial vehicle. The 3D laser scanner and UAV were used to generate digital elevation data of the study area, and the characteristics were analyzed. Through evaluation of the accuracy, it was confirmed that digital elevation data from a 3D laser scanner and UAV show accuracy within a 10 cm maximum, and it is suggested that it can be used for spatial information construction. In the future, collecting 3D elevation data from a 3D laser scanner and UAV is expected to be utilized as an efficient geospatial information-construction method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.139-145
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• 2006

The advantage of the lidar data is in fast acquisition and process time as well as in high accuracy and high point density. However lidar data itself is difficult to classify the earth surface because lidar data is in the form of irregularly distributed point clouds. In this study, we investigated land cover classification using both lidar data and optical image through a supervised classification method. Firstly, we generated 1m grid DSM and DEM image and then nDSM was produced by using DSM and DEM. In addition, we had made intensity image using the intensity value of lidar data. As for optical images, the red, blue, green band of CCD image are used. Moreover, a NDVI image using a red band of the CCD image and infrared band of IKONOS image is generated. The experimental results showed that land cover classification with lidar data and optical image together could reach to the accuracy of 74.0%. To improve classification accuracy, we further performed re-classification of shadow area and water body as well as forest and building area. The final classification accuracy was 81.8%.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.2
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• pp.11-17
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• 2009

Terrestrial LiDAR can measure high capacity 3D-topography coordinates and try to apply to various public works such as tunnel surveying, facility deformation surveying. This experiment is about how to calculate ground cutting volume because the stage of the earth work spend lots of money and time among civil engineering works. Surveying cutting area using Terrestrial LiDAR and then calculating cutting area in planned area comparing sectional plan before construction and planned section and LiDAR data. Also, the values of the calculating ground cutting volume by three different resolution LiDAR has are compared and analyzed.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.155-168
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• 2020

Open source is a key growth engine of the 4th industrial revolution, and the continuous development and use of various algorithms for image processing is expected. The purpose of this study is to examine the effectiveness of the UAS image processing open source based algorithm by comparing and analyzing the water reproduction and moving object filtering function and the time required for data processing in 3D reproduction. Five matching algorithms were compared based on recall and processing speed through the 'ANN-Benchmarks' program, and HNSW (Hierarchical Navigable Small World) matching algorithm was judged to be the best. Based on this, 108 algorithms for image processing were constructed by combining each methods of triangulation, point cloud data densification, and surface generation. In addition, the 3D reproduction and data processing time of 108 algorithms for image processing were studied for UAS (Unmanned Aerial System) images of a park adjacent to the sea, and compared and analyzed with the commercial image processing software 'Pix4D Mapper'. As a result of the study, the algorithms that are good in terms of reproducing water and filtering functions of moving objects during 3D reproduction were specified, respectively, and the algorithm with the lowest required time was selected, and the effectiveness of the algorithm was verified by comparing it with the result of 'Pix4D Mapper'.

• Journal of the Korean Geophysical Society
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• v.9 no.4
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• pp.377-386
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• 2006

There is an inseparable relation between human race and engineering work. As world developed into highly industrialized society, a diversity of large structures is being built up correspondently to limited topographical circumstance. Though large structures are national establishments which provide us with convenience of life, there are some disastrous possibilities which were never predicted such as ground subsidence and degradation. It is very difficult to analyze the volume of total metamorphosis with the relative displacement measurement system which is now used and it is impossible to know whether there is structural metamorphosis within a permissible range of design or not. In this research with an object of 13-year-old earthen dam, through generating point-cloud which has 3D spatial coordinates(x, y, z) of this dam by means of 3D Laser Scanning, we can get real configuration data of slanting surface of this dam with this method of getting a number of 3D spatial coordinates(x, y, z). It gives 3D spatial model to us and we can get various information of this dam such as the distance of slanting surface of dam, dimensions and cubic volume. It can be made full use of as important source material of reinforcement and maintenance works to detect previously the bulging of the dam through this research.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.2
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• pp.111-119
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• 2013

It is essential and fundamental to extract boundary information of target object via massive three-dimensional point data acquired from laser scanner. Especially extracting boundary information of manmade features such as buildings is quite important because building is one of the major components consisting complex contemporary urban area, and has artificially defined shape. In this research, extended ${\chi}$-algorithm using geometry information of point data was proposed to extract boundary information of building from three-dimensional point data consisting building. The proposed algorithm begins with composing Delaunay triangulation process for given points and removes edges satisfying specific conditions process. Additionally, to make whole boundary extraction process efficient, we used Sweep-hull algorithm for constructing Delaunay triangulation. To verify the performance of the proposed extended ${\chi}$-algorithm, we compared the proposed algorithm with Encasing Polygon Generating Algorithm and ${\alpha}$-Shape Algorithm, which had been researched in the area of feature extraction. Further, the extracted boundary information from the proposed algorithm was analysed against manually digitized building boundary in order to test accuracy of the result of extracting boundary. The experimental results showed that extended ${\chi}$-algorithm proposed in this research proved to improve the speed of extracting boundary information compared to the existing algorithm with a higher accuracy for detecting boundary information.