• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.3-10
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• 2016

In this paper, two reconstructed point cloud sets with the information of 3D features are analyzed. For a certain 3D reconstruction of the interior of a building, the first image set is taken from the sequential passive camera movement along the regular grid path and the second set is from the application of the laser scanning process. Matched key points over all images are obtained by the SIFT(Scale Invariant Feature Transformation) algorithm and are used for the registration of the point cloud data. The obtained results are point cloud number, average density of point cloud and the generating time for point cloud. Experimental results show the necessity of images from the additional sensors as well as the images from the camera for the more accurate 3D reconstruction of the interior of a building.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.204-211
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• 2021

In this study, information on vegetation was collected using a point cloud through a 3-D Terrestrial Lidar Scanner, and the physical shape was analyzed by reconfiguring the object based on the refined data. Each filtering step of the raw data was optimized, and the reference volume and the estimated results using the Alpha Shape and Voxel techniques were compared. As a result of the analysis, when the volume was calculated by applying the Alpha Shape, it was overestimated than reference volume regardless of data filtering. In addition, the Voxel method to be the most similar to the reference volume after the 8th filtering, and as the filtering proceeded, it was underestimated. Therefore, when re-implementing an object using a point cloud, internal voids due to the complex shape of the target object must be considered, and it is necessary to pay attention to the filtering process for optimal data analyzed in the filtering process.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.597-602
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• 2016

In image guided surgery, a patient registration process is a critical process for the successful operation, which is required to use pre-operative images such as CT and MRI during operation. Though several patient registration methods have been studied, we concentrate on one method that utilizes 3D surface measurement data in this paper. First, a hand-held 3D surface measurement device measures the surface of the patient, and secondly this data is matched with CT or MRI data using optimization algorithms. However, generally used ICP algorithm is very slow without a proper initial location and also suffers from local minimum problem. Usually, this problem is solved by manually providing the proper initial location before performing ICP. But, it has a disadvantage that an experience user has to perform the method and also takes a long time. In this paper, we propose a method that can accurately find the proper initial location automatically. The proposed method finds the proper initial location for ICP by converting 3D data to 2D curvature images and performing image matching. Curvature features are robust to the rotation, translation, and even some deformation. Also, the proposed method is faster than traditional methods because it performs 2D image matching instead of 3D point cloud matching.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.292-296
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• 2002

This study has been accomplished as a experimental study for field application of 3D Perspective Image Map creation using Digital Topographical Map and based on the Ortho-Projection Image which is generated from Satellite Overlay Images and the precise Relative Coordinates of longitude, latitude and altitude which is corrected by GCP(Ground Control Point). AS to Contour Lines Map which is created by Coordinate conversion of 1:5,000 Topographical Map, we firstly made Satellite Image Map to substitute for Digital Topographical Map through overlapping the original images on top of each Ortho-Projection Image created and checking the accuracy. In addition to 3D Image Map creation for 3D Terrain analysis of a target district, Slope Gradient Analysis, Aspect Analysis and Terrain Elevation Model generation, multidirectional 3D Image generation by DEM can be carried out through this study. This study is to develop a mapping technology with which we can generate 3D Satellite Images of a target district through the composition of Digital Maps and Facility Blueprint and arbitrarily create 3D Perspective Images of the target district from any view point.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.4
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• pp.105-112
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• 2018

This study aims to apply 3D scanning technology to the field of landscape planning design. Through this, 3D scans were conducted on Soswaewon Garden and Seongrakwon Gardens to find directions for traditional landscape planning and designs. The results as follows. First, the actual measurement of the traditional garden through a 3D scan confirmed that a precise three-dimensional modeling of ${\pm}3-5mm$ error was constructed through the merging of coordinate values based on point data acquired at each observation point and postprocessing. Second, as a result of the 3D survey, the Soswaewon Garden obtained survey data on Jewoldang House, Gwangpunggak Pavilion, the surrounding wall, stone axis, and Aeyangdan wall, while the Seongnakwon Garden obtained survey data on the topography, rocks and waterways around the Yeongbyeokji pond area. The above data have the advantage of being able to monitor the changing appearance of the garden. Third, spatial information developed through 3D scans could be developed with a three-dimensional drawing preparation and inspection tool that included precise real-world data, and this process ensured the economic feasibility of time and manpower in the actual survey and investigation of landscaping space. In addition, modelling with a three-dimensional 1:1 scale is expected to be highly efficient in that reliable spatial data can be maintained and reprocessed to a specific size depending on the size of the design. In addition, from a long-term perspective, the deployment of 3D scan data is easy to predict and simulate changes in traditional landscaping space over time.

• Korean Management Science Review
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• v.29 no.3
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• pp.169-182
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• 2012

This study examines the co-evolutionary process between open innovation and firms' absorptive capacity. The effects of open innovation can be maximized through the capacity to absorb the knowledge from the external sources such as universities, government-support research institute, and private R&D centers. This study used data of STEPI technology innovation survey conducted at 2002, 2005, and 2008 (3 points measures). The data were analyzed through a structural equation model. Results suggest that open innovation at t0 point influences positively the absorptive capacity at t1 point, which subsequently enhances the intention of open innovation at t2 point. This result suggests the existence of co-evolutionary process between open innovation and firms' absorptive capacity. When knowledge comes from universities, the co-evolution has sustained; whereas when knowledge comes from private firms' R&D centers, the co-evolution has not effected. Theoretical and practical implications are discussed.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.71-78
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• 2022

This paper proposes an algorithm for 3D object detection by processing point cloud data of 3D LiDAR. Unlike 2D LiDAR, 3D LiDAR-based data was too vast and difficult to process in three dimensions. This paper introduces various studies based on 3D LiDAR and describes 3D LiDAR data processing. In this study, we propose a method of processing data of 3D LiDAR using clustering techniques for object detection and design an algorithm that fuses with cameras for clear and accurate 3D object detection. In addition, we study models for clustering 3D LiDAR-based data and study hyperparameter values according to models. When clustering 3D LiDAR-based data, the DBSCAN algorithm showed the most accurate results, and the hyperparameter values of DBSCAN were compared and analyzed. This study will be helpful for object detection research using 3D LiDAR in the future.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.9
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• pp.401-410
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• 2016

3D registration is a computer vision technique of aligning multi-view range images with respect to a reference coordinate system. Various 3D registration algorithms have been introduced in the past few decades. Iterative Closest Point (ICP) is one of the widely used 3D registration algorithms, where various modifications are available nowadays. In the ICP-based algorithms, the closest points are considered as the corresponding points. However, this assumption fails to find matching points accurately when the initial pose between point clouds is not sufficiently close. In this paper, we propose a new method to solve this problem using the 6D distance (3D color space and 3D Euclidean distances). Moreover, a color segmentation-based adaptive sampling technique is used to reduce the computational time and improve the registration accuracy. Several experiments are performed to evaluate the proposed method. Experimental results show that the proposed method yields better performance compared to the conventional methods.

• 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.3
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• pp.99-106
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• 2019

Technology that cannot be excluded from 4th industry is self-driving sector. The self-driving sector can be seen as a key set of technologies in the fourth industry, especially in the DB sector is getting more and more popular as a business. The DB, which was previously produced and managed in two dimensions, is now evolving into three dimensions. Among the data obtained by Mobile Mapping System () to produce the HD MAP necessary for self-driving, Point Cloud, which is LiDAR data, is used as a DB because it contains accurate location information. However, at present, it is not widely used as a base data for 3D modeling in addition to HD MAP production. In this study, MMS Point Cloud was used to extract facilities around the road and to overlay the location to expand the usability of Point Cloud. Building utility poles and communication poles DB from Point Cloud and comparing road name address base and location, it is believed that the accuracy of the location of the facility DB extracted from Point Cloud is also higher than the basic road name address of the road, It is necessary to study the expansion of the facility field sufficiently.