• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.205-212
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• 2019

Scene graph is a kind of knowledge graph that represents both objects and their relationships found in a image. This paper proposes a 3D scene graph generation model for three-dimensional indoor environments. An 3D scene graph includes not only object types, their positions and attributes, but also three-dimensional spatial relationships between them, An 3D scene graph can be viewed as a prior knowledge base describing the given environment within that the agent will be deployed later. Therefore, 3D scene graphs can be used in many useful applications, such as visual question answering (VQA) and service robots. This proposed 3D scene graph generation model consists of four sub-networks: object detection network (ObjNet), attribute prediction network (AttNet), transfer network (TransNet), relationship prediction network (RelNet). Conducting several experiments with 3D simulated indoor environments provided by AI2-THOR, we confirmed that the proposed model shows high performance.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.185-185
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• 2010

문명 발전에 의해 생활공간이 더욱더 복잡해짐에 따라 현실 세계의 3차원 공간으로서의 이해가 필요해졌고, 3차원 공간을 효율적으로 관리 및 분석하기 위하여 3차원 GIS에 대한 중요성이 대두되기 시작하였다. 현재까지의 3차원 GIS는 1990년대에 들어서 3차원 가상도시의 등장으로 도시 모델의 3차원 가시화에 대해 많은 연구가 진행되어 왔다. 이러한 3차원 가상도시는 도시 관리 차원에서 도시공간의 표현, 분석, 모델링, 시뮬레이션 등을 통하여 도시정보의 체계적이고 효율적인 관리를 가능하게 한다. 3D GIS 기술의 활용 사례로는 3차원 도시 경관 분석 및 도시 계획, 3차원 시뮬레이션, 3차원 위치기반 서비스, 도시 행정 관리 등이 있다. 3차원 도시 경관 분석 및 도시 계획 활용 분야에서는 3차원 GIS를 활용하여 새롭게 건설되는 건물들이나 토지 이용 변경에 따른 경관 분석을 가상공간에서 시뮬레이션 분석함으로써 경관 변화 예측 및 적합한 경관 설계에 대한 분석을 한다. 3차원 시뮬레이션의 경우 환경오염도 분석, 최적의 입지선정, 개발 전후의 경관 차이, 각종 재난에 따른 위험도 분석, 경관과 일조량 분석 등을 미리 가상도시에 시뮬레이션 함으로서 불필요한 업무를 줄일 수 있고 발생할 수 있는 결과 값을 예상할 수 있다. 3차원 위치기반 서비스의 경우 자신의 위치를 비교적 정확히 측정함으로서 생소한 도시를 방문하거나 위치를 찾아갈 경우 최단 경로 및 최적 경로를 탐색할 수 있다. 앞으로의 3차원 GIS 기술 동향으로 유비쿼터스 환경에서의 시공간분석(Temporal GIS)과 3차원 실내공간인지 기술 개발 등으로 볼수있다. 시공간분석은 시공간 질의를 통한 공간정보의 시간에 다른 변화 계산 또는 예측된 결과를 분석할 수 있고, 시공간 분석에 사용되는 공간적, 시간적 정보의 조합은 적어도 3차원 이상의 정보를 나타내기 때문에 필요에 따라 다양한 결과를 산출 할 수 있다. 종합적으로 시공간분석은 GIS의 분석 범위를 넓혀 주며 실시간 GIS 응용시스템 개발을 위한 주요 과업이다. 3차원 실내공간정보 표현기법과 공간분석 및 공간인지 기법을 이용한 통한 유비쿼터스 환경에서의 3차원 실내공간인지 기술 개발을 통해 최근 공간정보를 기반으로 한 위치기반서비스내 u-서비스 즉, 인간 행태의 분석, 관리 및 응용을 지원하도록 한다 이는 위치기반서비스 사용자의 주변상황에 대한 인지능력을 향상시켜주고 최근 사회적 이슈가 되고 있는 U-City의 성공여부에 직접적인 영향을 미칠것이다. 이를 위해 다음과 같은 기술개발이 수행되어야 할것이다. 첫째로, 공간인지 기술을 위한 3차원 실내공간표현 방법 개발, 둘째로, 3차원 실내공간정보를 이용한 공간분석 및 공간인지 기법 개발, 마지막으로 유비쿼터스 환경에서 3차원 실내공간인지를 위한 미들웨어 개발을 통해서 공간정보의 활용을 macro-scale 공간에서 micro-scale로 확산될 것이다. 그러므로, 본 세미나를 통해서, 3차원 GIS의 활용의 보편화와 대중화를 위해서는 3차원 데이터 획득 및 처리 방법, 3차원 공간데이타베이스, 공간 분석 및 가시화에 대한 기술적 이슈들을 논하고자 한다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.3 s.303
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• pp.39-52
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• 2005

In this paper, a registration method is presented to register partial 3D point clouds, acquired from a multi-view camera, for 3D reconstruction of an indoor environment. In general, conventional registration methods require a high computational complexity and much time for registration. Moreover, these methods are not robust for 3D point cloud which has comparatively low precision. To overcome these drawbacks, a projection-based registration method is proposed. First, depth images are refined based on temporal property by excluding 3D points with a large variation, and spatial property by filling up holes referring neighboring 3D points. Second, 3D point clouds acquired from two views are projected onto the same image plane, and two-step integer mapping is applied to enable modified KLT (Kanade-Lucas-Tomasi) to find correspondences. Then, fine registration is carried out through minimizing distance errors based on adaptive search range. Finally, we calculate a final color referring colors of corresponding points and reconstruct an indoor environment by applying the above procedure to consecutive scenes. The proposed method not only reduces computational complexity by searching for correspondences on a 2D image plane, but also enables effective registration even for 3D points which have low precision. Furthermore, only a few color and depth images are needed to reconstruct an indoor environment.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.1
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• pp.1-9
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• 2007

3D data are in great demand for pedestrian navigation recently. For pedestrian navigation, we needs to reconstruct 3D model in detail from people's eye. In order to present spatial features in detail for pedestrian navigation, it is indispensable to develop 3D model not only in outdoor environment but also in indoor environment such as underground shopping complex. However, it is very difficult to acquire 3D data efficiently by mobile mapping without GPS. In this research, 3D shape was acquired by Laser scanner, and texture by CCD(Charge Coupled Device) sensor. Continuous changes position and attitude of sensors were measured by IMU(Inertial Measurement Unit). Moreover, IMU was corrected by relative orientation of CCD images without GPS(Global Positioning System). In conclusion, Reliable, quick, and handy method for acquiring 3D data for indoor environment is proposed by a combination of a digital camera and a laser scanner with IMU.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.169-175
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• 2016

Geofence is a virtual perimeter for a real-world geographical area, which could be statically or dynamically established the specified area if necessary. Many geofencing applications incorporate 2D(two-dimensional) map such as the Google map, allowing administrators to define boundaries on top of a satellite view of a specific geographical area. But these applications do not provide 3D(three-dimensional) spatial information as well as 2D location information no matter where indoor or outdoor. Therefore we propose a mechanism to identify indoor or outdoor location for 3D geofence, and implement 3D geofence using smartphone. The proposed mechanism identifies the position information on 3D geofence regardless of indoor or outdoor, inter-floor with only GPS and WiFi. In the near future, 3D geofence as well as LBS are promising applications that become possible when IoT can become organized and connected by location.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.11 s.114
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• pp.1058-1064
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• 2006

In this paper, the channel capacity of a specified wireless indoor multiple-input multiple-output(MIMO) channel is estimated by analyzing spatial characteristics of this channel using the three-dimensional ray tracing method, and a technique for deriving an optimized separation of multi-antenna elements is proposed. At first, the ray paths, the path losses, and the time-delay profile are computed using the three-dimensional ray tracing method in an indoor corridor environment, which has the line of sight(LOS) and non-line of sight(NLOS) regions. The ray tracing method is verified by a comparison between the computation results and the measurements which are obtained with dipole antennas, an amplifier and a network analyzer. Then, an MIMO system is positioned in the indoor channel environment and the ray paths and path losses are computed for four antenna-position combinations and various values of the antenna separation to obtain the channel capacity for the MIMO system. An optimum antenna-separation is derived by averaging the channel capacities of 100 receiver positions with four different antenna combinations.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.53-61
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• 2014

In this paper, we propose a method using an image received by the depth camera in order to separate the wall in a three-dimensional space indoor environment. Results of the paper may be used to provide valuable information on the three-dimensional space. For example, they may be used to recognize the indoor space, to detect adjacent objects, or to project a projector on the wall. The proposed method first detects a normal vector at each point by using the three dimensional coordinates of points. The normal vectors are then clustered into several groups according to similarity. The RANSAC algorithm is applied to separate out planes. The domain knowledge helps to determine the wall among planes in an indoor environment. This paper concludes with experimental results that show performance of the proposed method in various experimental environment.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.319-322
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• 2014

본 논문에서는 깊이 카메라로 받아들인 입력 영상의 3차원 공간 내에서 벽면을 분리하는 방법을 제안한다. 본 논문에서 제안하는 벽면이 구분된 영상은 벽면에 프로젝터를 투사하는 등의 3차원 공간 활용에 용이하다. 입력 영상에서의 좌표 점을 이용하여 법선 벡터를 검출하고, 법선 벡터를 통해 평면을 분리한다. 분리된 평면들을 실내 환경에서 알 수 있는 도메인 지식들에 기반하여 벽면으로 구분 된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.5D
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• pp.527-532
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• 2012

Terrestrial LiDAR emerges as a main mapping technology for indoor 3D cadastre, cultural heritage conservation and, building management in that it provides fast, accurate, and reliable 3D data. In this paper, a new 3D modeling method consisting of segmentation stage and outline extraction stage is proposed to develop indoor 3D model from the terrestrial LiDAR. In the segmentation process, RANSAC and a refinement grid is used to identify points that belong to identical planar planes. In the outline tracing process, a tracing grid and a data conversion method are used to extract outlines of indoor 3D models. However, despite of an improvement of productivity, the proposed approach requires an optimization process to adjust parameters such as a threshold of the RANSAC and sizes of the refinement and outline extraction grids. Furthermore, it is required to model curvilinear and rounded shape of the indoor structures.

• Journal of Convergence Society for SMB
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• v.4 no.2
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• pp.7-12
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• 2014

In this study, we present the investigation results using a 3D radio wave propagation simulator in indoor environments. Previous studies treat only the path loss between the transmitter and receiver in 2D geometry. We provide the simulation results of indoor propagation prediction based on various ITU-R Recommendations. Simulation results compared here indicate that 150MHz and 2GHz frequency bands give quite different characteristics in presented indoor geometry. Since the field intensity is affected by the loss at the wall, the transmitting power level is one of the key factor for receiving power.