• 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.

• ETRI Journal
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• v.46 no.4
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• pp.683-696
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• 2024

The point cloud provides viewers with intuitive geometric understanding but requires a huge amount of data. Moving Picture Experts Group (MPEG) has developed video-based point-cloud compression in the range of 300-700. As the compression rate increases, the complexity increases to the extent that it takes 101.36 s to compress one frame in an experimental environment using a personal computer. To realize real-time point-cloud compression processing, the direct patch projection (DPP) method proposed herein simplifies the complex patch segmentation process by classifying and projecting points according to their geometric positions. The DPP method decreases the complexity of the patch segmentation from 25.75 s to 0.10 s per frame, and the entire process becomes 8.76 times faster than the conventional one. Consequently, this proposed DPP method yields similar peak signal-to-noise ratio (PSNR) outcomes to those of the conventional method at reduced times (4.7-5.5 times) at the cost of bitrate overhead. The objective and subjective results show that the proposed DPP method can be considered when low-complexity requirements are required in lightweight device environments.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.680-691
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• 2021

Due to the recent developments of attaining 3D contents by using devices such as 3D scanners, the diversity of the contents being used in AR(Augmented Reality)/VR(Virutal Reality) fields is significantly increasing. There are several ways to represent 3D data, and using point clouds is one of them. A point cloud is a cluster of points, having the advantage of being able to attain actual 3D data with high precision. However, in order to express 3D contents, much more data is required compared to that of 2D images. The size of data needed to represent dynamic 3D point cloud objects that consists of multiple frames is especially big, and that is why an efficient compression technology for this kind of data must be developed. In this paper, a motion estimation and compensation method for dynamic point cloud objects using 3D DCT is proposed. This will lead to switching the 3D video frames into I frames and P frames, which ensures higher compression ratio. Then, we confirm the compression efficiency of the proposed technology by comparing it with the anchor technology, an Intra-frame based compression method, and 2D-DCT based V-PCC.

• Korean Journal of Computational Design and Engineering
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• v.21 no.3
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• pp.341-352
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• 2016

Manual 3D pipeline modeling from LiDAR scanned point cloud data is laborious and time-consuming process. This paper presents a method to extract the pipe, elbow and branch information which is essential to the automatic modeling of the pipeline connection. The pipe geometry is estimated from the point cloud data through the Hough transform and the elbow position is calculated by the medial axis intersection for assembling the nearest pair of pipes. The branch is also created for a pair of pipe segments by estimating the virtual points on one pipe segment and checking for any feasible intersection with the other pipe's endpoint within the pre-defined range of distance. As a result of the automatic modeling, a complete 3D pipeline model is generated by connecting the extracted information of pipes, elbows and branches.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.1-14
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• 2022

In order to conduct rapid, accurate and safe surveying at the excavation site, In this study, the possibility of underground facility survey using drones and the expected effect of 3D visualization were obtained as follows. Phantom4Pro 20MP drones have a 30m flight altitude and a redundant 85% flight plan, securing a GSD (Ground Sampling Distance) value of 0.85mm and 4points of GCP (Groud Control Point)and 2points of check point were calculated, and 7.3mm of ground control point and 11mm of check point were obtained. The importance of GCP was confirmed when measured with low-cost drones. If there is no ground reference point, the error range of X value is derived from -81.2 cm to +90.0 cm, and the error range of Y value is +6.8 cm to 155.9 cm. This study classifies point cloud data using the Pix4D program. I'm sorting underground facility data and road pavement data, and visualized 3D data of road and underground facilities of actual model through overlapping process. Overlaid point cloud data can be used to check the location and depth of the place you want through the Open Source program CloudCompare. This study will become a new paradigm of underground facility surveying.

• Architectural research
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• v.21 no.4
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• pp.111-116
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• 2019

The premise of this research is the recent advancement of Building Information Modeling(BIM) Technology and Laser Scanning Technology(3D Scanning). The purpose of the paper is to amplify the potential offered by the combination of BIM and Point Cloud Data (PCD) for structural analysis. Today, enormous amounts of construction site data can be potentially categorized and quantified through BIM software. One of the extraordinary strengths of BIM software comes from its collaborative feature, which can combine different sources of data and knowledge. There are vastly different ways to obtain multiple construction site data, and 3D scanning is one of the effective ways to collect close-to-reality construction site data. The objective of this paper is to emphasize the prospects of pre-scanning and post-scanning automation algorithms. The research aims to stimulate the recent development of 3D scanning and BIM technology to develop Scan-to-BIM. The paper will review the current issues of Scan-to-BIM tasks to achieve As-Built BIM and suggest how it can be improved. This paper will propose a method of coordinating and utilizing PCD for construction and structural analysis during construction.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.428-435
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• 2024

In this paper, a human activity regeneration (HAR) system based on multiple input multiple output frequency modulation continuous wave (MIMO FMCW) radar was designed and implemented. Using point cloud data from MIMO radar sensors has advantages in terms of privacy, safety, and accuracy. For the implementation of the HAR system, a customized neural network based on PointPillars and depthwise separate convolutional neural network (DS-CNN) was developed. By processing high-resolution point cloud data through a lightweight network, high accuracy and efficiency were achieved. As a result, the accuracy of 98.27% and the computational complexity of 11.27M multiply-accumulates (Macs) were achieved. In addition, the developed neural network model was implemented on Raspberry-Pi embedded system and it was confirmed that point cloud data can be processed at a speed of up to 8 fps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3375-3400
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• 2018

With the advent of database-as-a-service (DAAS) and cloud computing, more and more data owners are motivated to outsource their data to cloud database in consideration of convenience and cost. However, it has become a challenging work to provide security to database as service model in cloud computing, because adversaries may try to gain access to sensitive data, and curious or malicious administrators may capture and leak data. In order to realize privacy preservation, sensitive data should be encrypted before outsourcing. In this paper, we present a secure and practical system over encrypted cloud data, called QSDB (queryable and secure database), which simultaneously supports SQL query operations. The proposed system can store and process the floating point numbers without compromising the security of data. To balance tradeoff between data privacy protection and query processing efficiency, QSDB utilizes three different encryption models to encrypt data. Our strategy is to process as much queries as possible at the cloud server. Encryption of queries and decryption of encrypted queries results are performed at client. Experiments on the real-world data sets were conducted to demonstrate the efficiency and practicality of the proposed system.

• Cartoon and Animation Studies
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• s.48
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• pp.199-215
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• 2017

Recently, a drone is used for the general purpose application although the drone was builtfor the military purpose. A drone is actively used for the creation of contents, and an image acquisition. In this paper, we develop a 3D library module platform using 3D mesh model data, which is generated by a drone image and its point cloud. First, a lot of 2D image data are taken by a drone, and a point cloud data is generated from 2D drone images. A 3D mesh data is acquired from point cloud data. Then, we develop a service library platform using a transformed 3D data for multi-purpose uses. Our platform with 3D data can minimize the cost and time of contents creation for special effects during the production of a movie, drama, or documentary. Our platform can contribute the creation of experts for the digital contents production in the field of a realistic media, a special image, and exhibitions.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.