• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.563-566
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• 2022

본 논문에서는 증강현실에서 객체 증강 시, 특징점과 GPS를 이용하여 증강 위치를 효율적으로 보간할 수 있는 ICP(Iterative closest point) 매칭 기법을 제안한다. 다양한 환경에서 제한받지 않고 객체를 증강하기 위해 일반적으로 마커리스(Markerless) 방식을 사용하며, 대표적으로 평면 검출과 페이스 검출을 사용한다. 이는 현실과 자연스러운 동기화를 위한 것으로 계산은 작지만, 인식의 범위가 넓기 때문에 증강 위치에 대한 오차가 존재한다. 이러한 작은 오차는 특정 산업에서는 치명적일 수 있으며, 특히 건설이나 의료시설에서 발생하면 큰 사고로 이어진다. 객체를 증강 시킬 때 해당 환경에 대한 점 구름(Point cloud)을 수집하여 데이터베이스에 저장한다. 본 논문에서는 관측되는 점 구름과의 오차를 줄이기 위해 ICP 매칭 기법을 사용하며, 실린더 기반의 각도 보간을 이용하여 계산량을 줄인다. 결과적으로 특징점과 GPS를 이용하여 ICP 매칭 기법을 통해 효율적으로 처리함으로써, 증강 위치에 대한 정확도가 개선된 증강 방식을 보여준다.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.148-152
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• 2007

본 연구에서는 LIDAR 포인트 자료로부터 분리된 포인트 군집의 윤곽 포인트 추출을 위하여，가상격자를 이용한 검색 영역의 제한을 통한 윤곽 포인트 추출 방식을 제안하였으며 성능을 평가하기 위해 보편적으로 사용되는 TIN을 이용한 방식과 비교하였다. 실제 건물 포인트 자료에 대하여 적용한 결과 TIN을 이용한 방식보다 빠른 처리가 가능하며 시각적인 평가를 통해 결과물의 품질 면에서도 두 가지 방식이 거의 유사함을 확인할 수 있었다.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.111-114
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• 2003

In this paper, we propose a depth-based mesh modeling method to generate virtual environment. The proposed algorithm constructs mesh model from unorganized point cloud obtained from a multi-view camera. We separate the point cloud consisting objects from the background. Then, we apply triangulation to each object and background. Since the objects and the background are modeled independently, it is possible to construct effective virtual environment. The application of proposed modeling method is applicable to entertainment, such as movie and video game and effective virtual environment generation.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.498-500
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• 2019

Point cloud is a flexible set of points that can provide a scalable geometric representation which can be applied in different computer graphic task. We propose a method based on EdgeConv and densely connected layers to aggregate the features for better classification. Our proposed approach shows significant performance improvement compared to the state-of-the-art deep neural network-based approaches.

• Atmosphere
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• v.29 no.5
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• pp.609-614
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• 2019

Cloud seeding experiment has been proposed as a way to alleviate severe air pollution problem because, if successful, artificially produced precipitation through cloud seeding could scavenge out some portion of air pollutants. As a first step to verify the practicality of such experiment, seedability of the clouds observed in Seoul is assessed by examining statistical characteristics of some relevant meteorological variables. Analyses of 9 years of Korea Meteorological Agency Seoul station data indicate that as PM₁₀ mass concentration increases, cloud amount, liquid water path, and ice water path decrease, but the difference between temperature and dew point temperature tends to increase. Such finding suggests that cloud seeding becomes less feasible as air pollution becomes more severe in the Seoul metropolitan area, at least in a statistical sense. For some individual severe air pollution events, however, seedable clouds may exist and indeed cloud seeding experiments can be successful. Therefore, detailed investigation on cloud seedability for individual severe air pollution events are highly required to make a concrete assessment of cloud seeding as a way to alleviate severe air pollution problem.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.123-129
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• 2018

The purpose of this paper is to investigate the influence of utilitarian, hedonic and social motivations on continuance intention to use cloud services. To do this, this study built a research model and examined how ease of use, usefulness, enjoyment, social influence affect the continuance usage intention of cloud services. The survey method was used for this paper, and data from a total of 82 university students were used for the analysis. And structural equation model was used to analyze the data. The results of this empirical study is summarized as followings. First, enjoyment has a direct effect on the continuance usage intention of cloud services. Second, social influence has a direct effect on the continuance usage intention. Further, it will provide meaning suggestion point of the importance of not only utilitarian motivation but also hedonic and social motivations in establishing the use policy of cloud services.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.941-951
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• 2018

Utilization and demand of UAV (unmanned aerial vehicle) for the generation of 3D city model are increasing. In this study, we performed an experiment to adjustment position/orientation of UAV with incomplete attitude information and to extract point cloud data. In order to correct the attitude of the UAV, the rotation angle was calculated by using the continuous position information of UAV movements. Based on this, the corrected position/orientation information was obtained by applying IBA (Incremental Bundle Adjustment) based on photogrammetry. Each pair was transformed into an epipolar image, and the MDR (Multi-Dimensional Relaxation) technique was applied to obtain high precision DSM. Each extracted pair is aggregated and output in the form of a single point cloud or DSM. Using the DJI inspire1 and Phantom4 images, we can confirm that the point cloud can be extracted which expresses the railing of the building clearly. In the future, research will be conducted on improving the matching performance and establishing sensor models of oblique images. After that, we will continue the image processing technology for the generation of the 3D city model through the study of the extraction of 3D cloud It should be developed.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.276-284
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• 2022

In this paper, we implemented cloud rendering using WebRTC for high-quality AR and VR services. Cloud rendering is an applied technology of cloud computing. It efficiently handles the rendering of large volumes of 3D content. The conventional VR and AR service is a method of downloading 3D content. The download time is delayed as the 3D content capacity increases. Cloud rendering is a streaming method according to the user's point of view. Therefore, stable service is possible regardless of the 3D content capacity. In this paper, we implemented cloud rendering using WebRTC and analyzed its performance. We compared latency of 100MB, 300MB, and 500MB 3D AR content in 100Mbps and 300Mbps internet environments. As a result of the analysis, cloud rendering showed stable latency regardless of data volume. On the other hand, the conventional method showed an increase in latency as the data volume increased. The results of this paper quantitatively evaluate the stability of cloud rendering. This is expected to contribute to high-quality VR and AR services

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.787-800
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• 2021

Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to percept coordinate information of each point. Then, multiple 2D convolutional layers and a global max pooling layer are applied to extract global features. Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset. The proposed structure obtained higher accuracy compared to the existing methods. Experiments using the ModelNet10 dataset also prove that the difference in the point number of point clouds does not significantly influence on the proposed pointwise CNN structure.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.664-669
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• 2014

As the prevalence of cloud computing increases, various cloud service types have emerged, such as IaaS, PaaS, and SaaS. The growth and diversification of these cloud services has also resulted in the development of technology for cloud service brokers (CSBs), which serve as intermediate cloud services that can assist cloud tenants (users) in deploying services that fit their requirements. In order to broker cloud services, CSBs require the specification of structural models in order to facilitate the analysis and search for cloud services. In this study, we propose a variability-based service analysis model (SAM) that can be used to describe various cloud services. This model is based on the concept of variability in the software product line and represents the commonality and variability of cloud services by binding variants to each variation point that exists in the specification, quality, and pricing of the services. We also propose a virtual cloud bank architecture as a CSB that serves as an intermediate to provides tenants with appropriate cloud services based on the SAM.