• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.33-41
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• 2015

Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. Experimental results shows the qualitative and quantitative performance efficiently.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.2
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• pp.344-353
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• 2004

The purpose of this study was to offer the basis contributes to extract the standardized body data from 3D body measuring for women aged 60 and older. The WB4 of Cyberware was used, and the measuring program of 3D scanning data was 3DM. This study was focused to verify the reliability of 3D data and to offer the effective utilization of 3D measuring on the research for elderly women■s body. Subjects were 19 women aged 60 and older. And three women in late twenties and three dressforms for women were comparing subjects to analyze the signiscant difference by age or human body variable making error. First, 3D scanning was executed twice on each subject, but any significant difference was not appear between two scanning data. So we certifed we could get the consistent and reliable data from the 3D scanner used in this study. Second, the reliability of 3D measuring data was analyzed, and the error range which meant the difference between 3D data and traditional measuring data was analyzed. In elderly women, the significant difference between two data was appeared in 19 body parts. The 7 of 19 were concerned with armpit point. In young women, three significant difference were appeared, and in dressforms, any significant difference was not certified. From these results, we could certify that age or human body variable produced the difference between two data. Third, the data of elderly women from three measuring methods, 3D measuring, traditional measuring, and measuring on 2D photographs were compared. From the result, we found that the 3D measuring data was quite reliable for most body parts excluding some width parts. But in elderly women, there were some limitation to extract reliable data because of their unique body characteristics. In order to be a role of the effective measuring method, the 3D measuring protocol reflected the body characteristics of each age or gender had to be prepared.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.2 s.344
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• pp.24-33
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• 2006

The effective geometry processing IP architecture for mobile SoC that has real time 3D graphics acceleration performance in mobile information system is proposed. Base on the proposed IP architecture, we design the floating point arithmetic unit needed in geometry process and the floating point geometry processor supporting the 3D graphic international standard OpenGL-ES. The geometry processor is implemented by 160k gate area in a Xilinx-Vertex FPGA and we measure the performance of geometry processor using the actual 3D graphic data at 80MHz frequency environment The experiment result shows 1.5M polygons/sec processing performance. The power consumption is measured to 83.6mW at Hynix 0.25um CMOS@50MHz.

• The Journal of the Korean dental association
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• v.56 no.9
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• pp.479-490
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• 2018

3D printing technology supporting the specific patient medical services is actively being implemented in dentistry. The purpose of this study is to introduce the legal and institutional considerations to the medical practitioners in dentistry who must observe when they manufacture medical devices using 3D printers, and to provide a ways to activate and enhance their utilization in the domestic approval point of view for medical devices. Through the public data of government agencies and related organizations, the statutory system and compliance matters related to the manufacture of 3D printing medical devices have been examined and reviewed for the government's improvement efforts. Through the study, the government has been actively improving the system and making policy, but the active interest and participation of medical professionals and related workers are continually required to solve the problems which are scattered. 3D printing technology is expected to be more frequently utilized in the field of dentistry in near future. Therefore, it is essential to establish measures to improve the regulation through continuous cooperation with the related ministries with the long-term point of view enhancing smooth entry to the market for the medical devices by taking data from the continued research.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.307-310
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• 2000

The measuring method of large object using the pattern matching is discussed in the paper. It is hard and expensive to get the complete 3D data when the object is large or exceeds the limit of measuring devices. The large object is divided into several smaller areas and is scanned several times to get the data of all the pieces. These data are aligned to get the complete 3D data using the pattern matching method. The point pattern matching method and transform matrix algorithm are used for aligning. The laser slit beam and CCD camera is applied for experimental measurement. Visual C++ on Window98 is implemented in processing the algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12C
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• pp.984-994
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• 2010

Pose variation is a critical problem in face recognition. Three-dimensional(3D) face recognition techniques have been proposed, as 3D data contains depth information that may allow problems of pose variation to be handled more effectively than with 2D face recognition methods. This paper proposes a pose-normalized 3D face modeling method that translates and rotates any pose angle to a frontal pose using a plane fitting method by Singular Value Decomposition(SVD). First, we reconstruct 3D face data with stereo vision method. Second, nose peak point is estimated by depth information and then the angle of pose is estimated by a facial plane fitting algorithm using four facial features. Next, using the estimated pose angle, the 3D face is translated and rotated to a frontal pose. To demonstrate the effectiveness of the proposed method, we designed 2D and 3D face recognition experiments. The experimental results show that the performance of the normalized 3D face recognition method is superior to that of an un-normalized 3D face recognition method for overcoming the problems of pose variation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.479-493
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• 2016

LiDAR(Light Detection and Ranging) data acquired from ALS(Airborne Laser Scanner) has been intensively utilized to reconstruct object models. Especially, researches for 3D modeling from LiDAR data have been performed to establish high quality spatial information such as precise 3D city models and true orthoimages efficiently. To reconstruct object models from irregularly distributed LiDAR point clouds, sensor calibration, noise removal, filtering to separate objects from ground surfaces are required as pre-processing. Classification and segmentation based on geometric homogeneity of the features, grouping and representation of the segmented surfaces, topological analysis of the surface patches for modeling, and accuracy assessment are accompanied by modeling procedure. While many modeling methods are based on the segmentation process, this paper proposed to extract key points directly for building modeling without segmentation. The method was applied to simulated and real data sets with various roof shapes. The results demonstrate feasibility of the proposed method through the accuracy analysis.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.422-424
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• 2021

In this paper, it leads the approach of fusing multiple RGB cameras for visual objects recognition based on deep learning with convolution neural network and 3D Light Detection and Ranging (LiDAR) to observe the environment and match into a 3D world in estimating the distance and position in a form of point cloud map. The goal of perception in multiple cameras are to extract the crucial static and dynamic objects around the autonomous vehicle, especially the blind spot which assists the AV to navigate according to the goal. Numerous cameras with object detection might tend slow-going the computer process in real-time. The computer vision convolution neural network algorithm to use for eradicating this problem use must suitable also to the capacity of the hardware. The localization of classified detected objects comes from the bases of a 3D point cloud environment. But first, the LiDAR point cloud data undergo parsing, and the used algorithm is based on the 3D Euclidean clustering method which gives an accurate on localizing the objects. We evaluated the method using our dataset that comes from VLP-16 and multiple cameras and the results show the completion of the method and multi-sensor fusion strategy.