• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.818-820
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• 2022

포인트 클라우드 데이터는 자율 주행 기술, 가상 현실 및 증강 현실에서 사용될 3차원 미디어 중 하나로 각광 받고 있다. 국제 표준화 기구인 MPEG(Moving Picture Expert Group)에서는 포인트 클라우드 데이터의 효율적인 압축을 위해 G-PCC(Geometry-based Point Cloud Compression) 및 V-PCC(Video-based Point Cloud Compression)의 표준화를 진행 중에 있다. 그 중, G-PCC는 본래 단일 프레임의 압축을 수행하는 정지 영상 압축 방식이지만, LiDAR(Light Detection And Ranging) 센서를 통해 획득된 동적 포인트 클라우드 프레임에 대한 압축의 필요성이 대두됨에 따라 G-PCC 그룹에서는 Inter-EM(Exploratory Model)을 신설하여 LiDAR 포인트 클라우드 프레임의 압축에 관한 연구를 시작하였다. Inter-EM의 압축 비트스트림은 G-PCC 비트스트림과 마찬가지로 효과적인 전송 및 소비를 위해 미디어 저장 포맷인 ISOBMFF(ISO-based Media File Format)으로 캡슐화될 수 있다. 이때, 포인트 클라우드 프레임들은 자율 주행 등의 서비스에 사용하기 위해 시간 도메인뿐만 아니라 공간 도메인을 기반으로도 소비될 수 있어야 하지만, 공간 도메인을 기반으로 콘텐츠를 임의 접근하여 소비하는 방식은 기존 2D 영상의 시간 도메인 기반 소비방식과 차이로 인해 기존에 논의된 G-PCC 캡슐화 방안만으로는 지원이 제한된다. 이에, 본 논문에서는 G-PCC 콘텐츠를 공간 도메인에 따라 소비하기 위한 ISOBMFF 캡슐화 방안에 대한 파일 포맷을 제안하고자 한다.

• Korean Journal of Construction Engineering and Management
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• v.25 no.3
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• pp.37-46
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• 2024

Recently, 3D laser scanning technology, which can collect accurate and quick information on phenomena, has been attracting attention among smart construction technologies. 3D laser scanning technology can obtain information most similar to reality at construction sites. In this study, we would like to apply a new member identification method to an actual building and present the possibility of applying point cloud data, which can be collected using 3D laser scanning technology, to measuring progress at construction sites. In order to carry out the research, we collected location information for component identification from BIM, set a recognition margin for the collected location information, and proceeded to identify the components that make up the building from point cloud data. Research results We confirmed that the columns, beams, walls, and slabs that make up a building can be identified from point cloud data. The identification results can be used to confirm all the parts that have been completed in the actual building, and can be used in conjunction with the unit price of each part in the project BOQ for prefabricated calculations. In addition, the point cloud data obtained through research can be used as accurate data for quality control monitoring of construction sites and building maintenance management. The research results can contribute to improving the timeliness and accuracy of construction information used in future project applications.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.166-168
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• 2020

MPEG 비디오 그룹은 MPEG-I 표준의 일부로 포인트 클라우드(Point Cloud) 압축을 위한 비디오 기반 포인트 클라우드 부호화(V-PCC)와 몰입형(immersive) 비디오 압축을 위한 MPEG Immersive Video(MIV) 표준을 개발하고 있다. 최근에는 포인트 클라우드 및 몰입형 비디오와 같은 체적형(volumetric) 비디오를 모두 압축할 수 있도록 V-PCC 와 MIV 를 통합한 V3C(Visual Volumetric Video-based Coding) 표준화를 진행하고 있다. 본 논문에서는 V3C 코덱을 사용한 3DoF+(3 Degree of Freedom plus) 비디오 부호화 방안을 분석한다. 또한 V3C 코덱의 2D 코덱으로 기존 HEVC 대신 VVC 를 사용할 경우의 부호화 성능 향상을 분석한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.301-310
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• 2013

The aim of the present study is to enhance query speed of large 3D point cloud indexed in octree by parallel query using multi-cores. Especially, it is focused on developing methods of accessing multiple leaf nodes in octree concurrently to query points residing within a radius from a given coordinates. To the end, two parallel query methods are suggested using different strategies to distribute query overheads to each core: one using automatic division of 'for routines' in codes controlled by OpenMP and the other considering spatial division. Approximately 18 million 3D points gathered by a terrestrial laser scanner are indexed in octree and tested in a system with a 8-core CPU to evaluate the performances of a non-parallel and the two parallel methods. In results, the performances of the two parallel methods exceeded non-parallel one by several times and the two parallel rivals showed competing aspects confronting various query radii. Parallel query is expected to be accelerated by anticipated improvements of distribution strategies of query overhead to each core.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.481-486
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• 2014

This study aims at enhancing the performance of file-referring octree, suggested by Han(2014), for efficiently querying huge 3D point clouds, acquired by the 3D terrestrial laser scanning. Han's method(2014) has revealed a problem of heavy declining in query speed, when if it was applied on a very long tunnel, which is the lengthy and narrow shaped anisometric structure. Hereupon, the shape of octree has been analyzed of its influence on the query efficiency with the testing method of generating an independent octree in each isometric subdivision of 3D object boundary. This method tested query speed and main memory usage against the conventional single octree method by capturing about 300 million points in a very long tunnel. Finally, the testing method resulted in which twice faster query speed is taking similar size of memory. It is also approved that the conclusive factor influencing the query speed is the destination level, but the query speed can still increase with more proximity to isometric bounding shape of octree. While an excessive unbalance of octree shape along each axis can heavily degrade the query speed, the improvement of octree shape can be more effectively enhancing the query speed than increasement of destination level.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1199-1206
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• 2015

This paper introduces an algorithm for computing similarity between two poses in the motion capture data with different scale of skeleton, different number of joints and different joint names. The proposed algorithm first performs the topological analysis on the skeleton hierarchy for classifying the joints into more meaningful groups. The global joints positions of each joint group then are aggregated into a point cloud. The number of joints and their positions are automatically adjusted in this process. Once we have two point clouds, the algorithm finds an optimal 2D transform matrix that transforms one point cloud to the other as closely as possible. Then, the similarity can be obtained by summing up all distance values between two points clouds after applying the 2D transform matrix. After some experiment, we found that the proposed algorithm is able to compute the similarity between two poses regardless of their scale, joint name and the number of joints.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.204-211
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• 2021

In this study, information on vegetation was collected using a point cloud through a 3-D Terrestrial Lidar Scanner, and the physical shape was analyzed by reconfiguring the object based on the refined data. Each filtering step of the raw data was optimized, and the reference volume and the estimated results using the Alpha Shape and Voxel techniques were compared. As a result of the analysis, when the volume was calculated by applying the Alpha Shape, it was overestimated than reference volume regardless of data filtering. In addition, the Voxel method to be the most similar to the reference volume after the 8th filtering, and as the filtering proceeded, it was underestimated. Therefore, when re-implementing an object using a point cloud, internal voids due to the complex shape of the target object must be considered, and it is necessary to pay attention to the filtering process for optimal data analyzed in the filtering process.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.611-622
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• 2020

As the advancement of technologies on indoor positioning systems and measuring devices such as LiDAR (Light Detection And Ranging) and cameras, the demands on analyzing and searching indoor spaces and visualization services via virtual and augmented reality have rapidly increasing. To this end, it is necessary to model 3D objects from measured data from real-world structures. In addition, it is important to store these structured data in standardized formats to improve the applicability and interoperability. In this paper, we propose a method to construct IndoorGML data, which is an international standard for indoor modeling, from point cloud data acquired from LiDAR sensors. After examining considerations that should be addressed in IndoorGML data, we present a construction method, which consists of free space extraction and connectivity detection processes. With experimental results, we demonstrate that the proposed method can effectively reconstruct the 3D model from point cloud.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.323-324
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• 2023

본 논문에서는 다양한 복원 인공지능 알고리즘 중 하나인 3차원 복원 기술은 실제로 존재하는 물체의 2차원적인 픽셀을 3차원의 형태로 구현하여 형상화한다. 정확한 3차원 정보 처리가 요구됨에 따라 포인트 클라우드로 표현되는 데이터를 통해 정확한 쿨체의 크기 정보나 좌표 정보를 표시할 수 있다. 데이터의 픽셀을 분석하여 3차원의 형태로 구현할 것을 정의하는 복셀화(Voxelization) 알고리즘 전처리 과정을 통해 3차원 복원 기술 3D-GAN 활용으로 3차원 형태 형상화를 하였다. 본 논문에서는 3차원 복원 알고리즘 통하여 2차원 포인트 클라우드를 분석해 3차원 형태로 복원하는 기술에 대한 설명한다.

• Broadcasting and Media Magazine
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• v.26 no.2
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• pp.31-45
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• 2021

본 고는 ISO/IEC JTC 1/SC 29/WG 7 MPEG(Moving Picture Experts Group) 3DG(3D Graphics coding) 그룹에서 진행되고 있는 포인트 클라우드 데이터 압축 표준 기술 중 하나인 G-PCC(Geometry-based Point Cloud Compression) 표준에 대하여 설명하고자 한다. G-PCC는 포인트 클라우드의 기하 정보와 속성 정보를 3차원 공간에서 서로 다른 기술을 이용하여 압축하는 표준으로, 무손실 압축 방법의 경우 10:1의 압축율을 제공하고 손실 압축의 경우 35:1 정도의 압축율을 보인다. 본 고에서는 G-PCC의 기하 정보와 속성 정보의 압축 방법을 상세히 설명하고 같은 기능을 수행하는 압축 기술 간의 성능을 비교하고자 한다.