• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1283-1288
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• 2009

In general, a 3D computer graphic model is being used to generate a digital hologram as theinput information because the 3D information of an object can be extracted from a 3D model, easily. The 3D information of a real scene can be extracted by using a depth camera. The 3D information, point cloud, corresponding to real scene is extracted from a taken image pair, a gray texture and a depth map, by a depth camera. The extracted point cloud is used to generate a digital hologram as input information. The digital hologram is generated by using the coherent holographic stereogram, which is a fast digital hologram generation algorithm based on segmentation. The generated digital hologram using the taken image pair by a depth camera is reconstructed by the Fresnel approximation. By this method, the digital hologram corresponding to a real scene or a real object could be generated by using the fast digital hologram generation algorithm. Furthermore, experimental results are satisfactory.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.33-36
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• 2021

In this paper, we propose a new method for improving compression efficiency of immersive video using reinforcement learning. Immersive video means a video that a user can directly experience, such as 3DOF+ videos and Point Cloud videos. It has a vast amount of information due to their characteristics. Therefore, lots of compression methods for immersive video are being studied, and generally, a method, which projects an 3D image into 2D image, is used. However, in this process, a region where information does not exist is created, and it can decrease the compression efficiency. To solve this problem, we propose the reinforcement learning-based filling method with considering the characteristics of images. Experimental results show that the performance is better than the conventional padding method.

• Korean Journal of Agricultural Science
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• v.49 no.3
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• pp.483-493
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• 2022

It is important to improve the efficiency of plant breeding and crop yield to fulfill increasing food demands. In plant phenotyping studies, the capability to correlate morphological traits such as plant height, stem diameter, leaf length, leaf width, leaf angle and size of panicle of the plants has an important role. However, manual phenotyping of plants is prone to human errors and is labor intensive and time-consuming. Hence, it is important to develop techniques that measure plant phenotypic traits accurately and rapidly. The aim of this study was to determine the feasibility of point cloud data based on a 3D light detection and ranging (LiDAR) system for plant phenotyping. The obtained results were then verified through manually acquired data from the sorghum samples. This study measured the plant height, plant crown diameter and the panicle height and diameter. The R² of each trait was 0.83, 0.94, 0.90, and 0.90, and the root mean square error (RMSE) was 6.8 cm, 1.82 cm, 5.7 mm, and 7.8 mm, respectively. The results showed good correlation between the point cloud data and manually acquired data for plant phenotyping. The results indicate that the 3D LiDAR system has potential to measure the phenotypes of sorghum in a rapid and accurate way.

• Journal of Urban Science
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• v.7 no.2
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• pp.53-60
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• 2018

Digital Twin is a technology that creates a photocopy of real-world objects on a computer and analyzes the past and present operational status by fusing the structure, context, and operation of various physical systems with property information, and predicts the future society's countermeasures. In particular, 3D rendering technology (UAS, LiDAR, GNSS, etc.) is a core technology in digital twin. so, the research and application are actively performed in the industry in recent years. However, UAS (Unmanned Aerial System) and LiDAR (Light Detection And Ranging) have to be solved by compensating blind spot which is not reconstructed according to the object shape. In addition, the terrestrial LiDAR can acquire the point cloud of the object more precisely and quickly at a short distance, but a blind spot is generated at the upper part of the object, thereby imposing restrictions on the forward digital twin modeling. The UAS is capable of modeling a specific range of objects with high accuracy by using high resolution images at low altitudes, and has the advantage of generating a high density point group based on SfM (Structure-from-Motion) image analysis technology. However, It is relatively far from the target LiDAR than the terrestrial LiDAR, and it takes time to analyze the image. In particular, it is necessary to reduce the accuracy of the side part and compensate the blind spot. By re-optimizing it after fusion with UAS and Terrestrial LiDAR, the residual error of each modeling method was compensated and the mutual correction result was obtained. The accuracy of fusion-based 3D model is less than 1cm and it is expected to be useful for digital twin construction.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1027-1036
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• 2020

In this study, a cloud-based processing method using Agisoft Metashape, a commercial software, and Amazon web service, a cloud computing service, is introduced and evaluated to quickly generate high-precision 3D realistic data from large volume UAV images acquired in disaster sites. Compared with on-premises method using a local computer and cloud services provided by Agisoft and Pix4D, the processes of aerial triangulation, 3D point cloud and DSM generation, mesh and texture generation, ortho-mosaic image production recorded similar time duration. The cloud method required uploading and downloading time for large volume data, but it showed a clear advantage that in situ processing was practically possible. In both the on-premises and cloud methods, there is a difference in processing time depending on the performance of the CPU and GPU, but notso much asin a performance benchmark. However, it wasfound that a laptop computer equipped with a low-performance GPU takes too much time to apply to in situ processing.

• ETRI Journal
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• v.43 no.4
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• pp.603-616
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• 2021

Over the last few years, autonomous vehicles have progressed very rapidly. The odometry technique that estimates displacement from consecutive sensor inputs is an essential technique for autonomous driving. In this article, we propose a fast, robust, and accurate odometry technique. The proposed technique is light detection and ranging (LiDAR)-based direct odometry, which uses a spherical range image (SRI) that projects a three-dimensional point cloud onto a two-dimensional spherical image plane. Direct odometry is developed in a vision-based method, and a fast execution speed can be expected. However, applying LiDAR data is difficult because of the sparsity. To solve this problem, we propose an SRI generation method and mathematical analysis, two key point sampling methods using SRI to increase precision and robustness, and a fast optimization method. The proposed technique was tested with the KITTI dataset and real environments. Evaluation results yielded a translation error of 0.69%, a rotation error of 0.0031°/m in the KITTI training dataset, and an execution time of 17 ms. The results demonstrated high precision comparable with state-of-the-art and remarkably higher speed than conventional techniques.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.849-858
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• 2019

Recent developments in computer graphics technology and image processing technology have increased interest in point cloud technology for inputting real space and object information as three-dimensional data. In particular, point cloud technology can accurately provide spatial information, and has attracted a great deal of interest in the field of autonomous vehicles and AR (Augmented Reality)/VR (Virtual Reality). However, in order to provide users with 3D point cloud contents that require more data than conventional 2D images, various technology developments are required. In order to solve these problems, an international standardization organization, MPEG(Moving Picture Experts Group), is in the process of discussing efficient compression and transmission schemes. In this paper, we provide a region division transfer method of 3D point cloud content through extension of existing MPEG-DASH (Dynamic Adaptive Streaming over HTTP)-SRD (Spatial Relationship Description) technology, quality parameters are further defined in the signaling message so that the quality parameters can be selectively determined according to the user's request. We also design a verification platform for ROUTE (Real Time Object Delivery Over Unidirectional Transport)/DASH based heterogeneous network environment and use the results to validate the proposed technology.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.451-456
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• 2018

UAVs (Unmanned Aerial Vehicles) are widely used for maintenance and monitoring of facilities. It is necessary to acquire a high-resolution image for evaluating the appearance state of the facility in safety inspection. In addition, it is essential to acquire the video data in order to acquire data over a wide area rapidly. In general, since video data does not include position information, it is difficult to analyze the actual size of the inspection object quantitatively. In this study, we evaluated the utilization of 3D point cloud data of bridges using a matching between video frames and reference photos. The drones were used to acquire video and photographs. And exterior orientations of the video frames were generated through feature point matching with reference photos. Experimental results showed that the accuracy of the video frame data is similar to that of the reference photos. Furthermore, the point cloud data generated by using video frames represented the shape and size of bridges with usable accuracy. If the stability of the product is verified through the matching test of various conditions in the future, it is expected that the video-based facility modeling and inspection will be effectively conducted.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.265-268
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• 2022

Video-based Point Cloud Compression(V-PCC) 부호화기의 세그먼트 정제(Refining segmentation) 과정은 3D 세그먼트를 2D 패치 데이터로 효율적으로 변환하기 위한 V-PCC 부호화기의 핵심 파트이지만, 많은 연산량을 필요로 하는 모듈이다. 때문에 이미 TMC2 에 Fast Grid-based refine segmentation 과정이 구현되어 있으나, 아직도 세그먼트 정제 기술의 연산량은 매우 높은 편이다. 본 논문에서는 현재 TMC2 에 구현되어 있는 Fast Gridbased Refine Segmentation 을 살펴보고, 복셀(Voxel) 타입에 따른 특성에 맞춰 두 가지 조건을 추가하는 고속화 알고리즘을 제안한다. 실험 결과 압축성능(BD-BR)은 TMC2 와 거의 차이를 보이지 않았지만, 모듈 단위 평균 10% 연산량이 절감되는 것을 확인하였다.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.181-190
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• 2021

In this study, a large-scale levee breach experiment from lateral overflow was conducted to verify the effect of the new reinforcement method applied to the levee's surface. The new method could prevent levee failure and minimize damage caused by overflow in rivers. The levee was designed at the height of 2.5 m, a length of 12 m, and a slope of 1:2. A new material mixed with biopolymer powder, water, weathered granite, and loess in an appropriate ratio was sprayed on the levee body's surface at a thickness of about 5 cm, and vegetation recruitment was also monitored. At the Andong River Experiment Center, a flow (4 ㎥/s) was introduced from the upstream of the A3 channel to induce the lateral overflow. The change of lateral overflow was measured using an acoustic doppler current profiler in the upstream and downstream. Additionally, cameras and drones were used to analyze the process of the levee breach. Also, a new method using 3D point cloud for calculating the surface loss rate of the levee over time was suggested to evaluate the performance of the levee reinforcement method. It was compared to existing method based on image analysis and the result was reasonable. The proposed 3D point cloud methodology could be a solution for evaluating the performance of levee reinforcement methods.