• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.2
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• pp.123-130
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• 2016

With the development of fast construction mode in shipbuilding market, the demand on accuracy management of hull is becoming higher and higher in shipbuilding industry. In order to enhance production efficiency and reduce manufacturing cycle time in shipbuilding industry, it is important for shipyards to have the accuracy of ship components evaluated efficiently during the whole manufacturing cycle time. In accurate shipbuilding process, block accuracy is the key part, which has significant meaning in shortening the period of shipbuilding process, decreasing cost and improving the quality of ship. The key of block accuracy control is to create a integrate block accuracy controlling system, which makes great sense in implementing comprehensive accuracy controlling, increasing block accuracy, standardization of proceeding of accuracy controlling, realizing "zero-defect transferring" and advancing non-allowance shipbuilding. Generally, managers of accuracy control measure the vital points at section surface of block by using the heavy total station, which is inconvenient and time-consuming for measurement of vital points. In this paper, a new measurement method based on point clouds technique has been proposed. This method is to measure the 3D coordinates values of vital points at section surface of block by using 3D scanner, and then compare the measured point with design point based on ICP algorithm which has an allowable error check process that makes sure that whether or not the error between design point and measured point is within the margin of error.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.733-734
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• 2016

This study proposes a novel approach for ground segmentation of 3D point cloud. We combine two techniques: gradient threshold segmentation, and mean height evaluation. Acquired 3D point cloud is represented as a graph data structures by exploiting the structure of 2D reference image. The ground parts nearing the position of the sensor are segmented based on gradient threshold technique. For sparse regions, we separate the ground and nonground by using a technique called mean height evaluation. The main contribution of this study is a new ground segmentation algorithm which works well with 3D point clouds from various environments. The processing time is acceptable and it allows the algorithm running in real time.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.854-861
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• 2005

Studied accuracy and practical use possibility of joint measurement that using 3D laser scanner to rock slope. Measured joint of Rock slope and comparison applied 3 dimension laser scanner and clinometer. 3D laser scanning system preserves on computer calculating to 3 dimension coordinate scaning laser to object. and according to laser measurement method of interior, produce correct vector value from charge-coupled device(CCD) or laser reciver and telegram register and time measuring equipment. Create of object x, y, z point coordinates to 3 dimension space of computer. Such 3 dimension point datum (Point Clouds) forms relocate position informations that exist to practical space to computer space. Practical numerical values related between each other. Compared joint distribution and direction that measured by laser scanner and clinometer. By the result, Distribution of joint projected almost equally. Could get more joint datas by measurement of 3 dimension scanner than measured by clinometer. Therefore, There is effect that objectification of rock slope investigation data, shortening of investigation periods, investigation reduction of cost. could know that it is very effective method in joint measuring.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.507-516
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• 2007

Segmentation and organization of the LiDAR (Light Detection and Ranging) data of the Earth's surface are difficult tasks because the captured LiDAR data are composed of irregularly distributed point clouds with lack of semantic information. The reason for this difficulty in processing LiDAR data is that the data provide huge amount of the spatial coordinates without topological and/or relational information among the points. This study introduces LiDAR data segmentation technique by utilizing histograms of the LiDAR height image data and analyzing roof shape for 3D reconstruction and visualization of the buildings. One of the advantages in utilizing LiDAR height image data is no registration required because the LiDAR data are geo-referenced and ortho-projected data. In consequence, measurements on the image provide absolute reference coordinates. The LiDAR image allows measurement of the initial building boundaries to estimate locations of the side walls and to form the planar surfaces which represent approximate building footprints. LiDAR points close to each side wall were grouped together then the least-square planar surface fitting with the segmented point clouds was performed to determine precise location of each wall of an building. Finally, roof shape analysis was performed by accumulated slopes along the profiles of the roof top. However, simulated LiDAR data were used for analyzing roof shape because buildings with various shapes of the roof do not exist in the test area. The proposed approach has been tested on the heavily built-up urban residential area. 3D digital vector map produced by digitizing complied aerial photographs was used to evaluate accuracy of the results. Experimental results show efficiency of the proposed methodology for 3D building reconstruction and large scale digital mapping especially for the urban area.

• Journal of the Korea Computer Graphics Society
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• v.13 no.4
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• pp.21-27
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• 2007

The 3D Delaunay triangulation is the most widely used method for the mesh creation via the triangulation of a 3D point cloud. However, the method involves a heavy computational cost and, hence, in many interactive applications, it is not appropriate for surface triangulation. In this paper, we propose an efficient triangulation method to create a surface mesh from a 3D point cloud. We divide a set of object points into multiple subsets and apply the 2D Delaunay triangulation to each subset. A given 3D point cloud is cut into slices with respect to the OBB(Oriented Bounding Box) of the point set. The 2D Delaunay triangulation is applied to each subset producing a partial triangulation. The sum of the partial triangulations constitutes the global mesh. As a postprocessing process, we eliminate false edges introduced in the split steps of the triangulation and improve the results. The proposed method can be effectively applied to various image-based modeling applications.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10b
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• pp.202-205
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• 2007

본 논문에서는 3D 점집합으로부터 3D 메쉬를 생성하는 효율적인 기법을 소개한다. 대표적인 3D 삼각화 방법으로 3D Delaunay 삼각화 기법이 있으나 물체의 표면만을 고려한 메쉬 생성을 위한 방법으로 비효율적인 측면이 있다. 본 논문에서는 적은 계산량으로 물체의 표면 메쉬를 생성하는 기법을 소개한다. 물체의 각 영역을 분할하고 각 영역에 대해서 2D Delaunay 삼각화를 적용하여 3D 메쉬 구조를 얻는다. 3차원 점 집합에 대해 OBB를 계산하고 이를 기준으로 점집합을 다양한 각도에서 자르고 각 부분 점집합에 대해서 2D Delaunay 삼각화를 실시한다. 절단하는 각도의 간격이나 폭은 원래의 3D 점집합에서의 가장 가까운 이웃점들까지의 평균 거리를 이용하여 결정하도록 하였다. 후처리 과정으로 삼각 분할과정에서 잘못된 에지의 연결을 제거함으로써 객체의 삼각 분할 결과를 향상시킨다. 제안된 메쉬 생성 기법은 다양한 영상 기반 모델링 응용에서 효과적으로 적용될 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1199-1206
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• 2015

This paper introduces an algorithm for computing similarity between two poses in the motion capture data with different scale of skeleton, different number of joints and different joint names. The proposed algorithm first performs the topological analysis on the skeleton hierarchy for classifying the joints into more meaningful groups. The global joints positions of each joint group then are aggregated into a point cloud. The number of joints and their positions are automatically adjusted in this process. Once we have two point clouds, the algorithm finds an optimal 2D transform matrix that transforms one point cloud to the other as closely as possible. Then, the similarity can be obtained by summing up all distance values between two points clouds after applying the 2D transform matrix. After some experiment, we found that the proposed algorithm is able to compute the similarity between two poses regardless of their scale, joint name and the number of joints.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.166-173
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• 2020

Multi-view images, which are widely used for providing free-viewpoint services, can enhance the quality of synthetic views when the number of views increases. However, there needs an efficient representation method because of the tremendous amount of data. In this paper, we propose a method for generating point cloud data for the efficient representation of multi-view color and depth images. The proposed method conducts sequential reconstruction of point clouds at each viewpoint as a method of deleting duplicate data. A 3D point of a point cloud is projected to a frame to be reconstructed, and the color and depth of the 3D point is compared with the pixel where it is projected. When the 3D point and the pixel are similar enough, then the pixel is not used for generating a 3D point. In this way, we can reduce the number of reconstructed 3D points. Experimental results show that the propose method generates a point cloud which can generate multi-view images while minimizing the number of 3D points.

• Advances in Computational Design
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• v.3 no.4
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• pp.319-338
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• 2018

High-performing architecture should be designed by taking into account the mutual dependency between the new building and the local context. The performative architecture plays an important role to avert any unforeseen failures after the building has been built; particularly ones related to the microclimate impacts that affect the human comfort. The use of the concept of solar envelopes helps designers to construct the developable mass of the building design considering the solar access and the site obstruction. However, the current analysis method using solar envelopes lack in terms of integrating the detailed information of the existing context during the simulation process. In architectural design, often the current site modelling not only absent in preserving the complex geometry but also information on the surface characteristics. Currently, the emerging applications of point clouds offer a great possibility to overcome these limitations, since they include the attribute information such as XYZ as the position information and RGB as the color information. This study particularly presents a comparative analysis between the manually built 3D models and the models generated from the point cloud data. The modelling comparisons focus on the relevant factors of solar radiation and a set of simulation to calculate the performance indicators regarding selected portions of the models. The experimental results emphasize an introduction of the design approach and the dataset visibility of the 3D existing environments. This paper ultimately aims at improving the current architectural decision of support environment means, by increasing the correspondence between the digital models for performance analysis and the real environments (context of design) during the conceptual design phase.