• Journal of Web Engineering
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• v.21 no.2
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• pp.425-442
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• 2021

Media technology has been developed to give users a sense of immersion. Recent media using 3D spatial data, such as augmented reality and virtual reality, has attracted attention. A point cloud is a data format that consists of a number of points, and thus can express 3D media using coordinates and color information for each point. Since a point cloud has a larger capacity than 2D images, a technology to compress the point cloud is required, i.e., standardized in the international standard organization MPEG as a video-based point cloud compression (V-PCC). V-PCC decomposes 3D point cloud data into 2D patches along orthogonal directions, and those patches are placed into a 2D image sequence, and then compressed using existing 2D video codecs. However, data loss may occur while converting a 3D point cloud into a 2D image sequence and encoding this sequence using a legacy video codec. This data loss can cause deterioration in the quality of a reconstructed point cloud. This paper proposed a method of enhancing a reconstructed point cloud by applying a super resolution network to the 2D patch image sequence of a 3D point cloud.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.4
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• pp.335-342
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• 2012

3D point cloud is widely used in Architecture, Civil Engineering, Medical, Computer Graphics, and many other fields. Due to the improvement of 3D laser scanner, a massive 3D point cloud whose gigantic file size is bigger than computer's memory requires efficient preprocessing and visualization. We suggest a data structure to solve the problem; a 3D point cloud is gradually subdivided by arbitrary-sized cube grids structure and corresponding point cloud subsets generated by the center of each grid cell are achieved while preprocessing. A massive 3D point cloud file is tested through two algorithms: QSplat and ours. Our algorithm, grid-based, showed slower speed in preprocessing but performed faster rendering speed comparing to QSplat. Also our algorithm is further designed to editing or segmentation using the original coordinates of 3D point cloud.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.836-838
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• 2022

3D Point Cloud는 3D 콘텐츠를 더욱 실감 나게 표현하기 위한 데이터 포맷이다. Point Cloud 데이터는 3차원 공간상에 존재하는 데이터로 기존의 2D 영상에 비해 거대한 용량을 가지고 있다. 최근 대용량 Point Cloud의 3D 데이터를 압축하기 위해 V-PCC(Video-based Point Cloud Compression)와 같은 다양한 방법이 제시되고 있다. 따라서 Point Cloud 데이터의 원활한 전송 및 저장을 위해서는 V-PCC와 같은 압축 기술이 요구된다. V-PCC는 Point Cloud의 데이터들을 Patch로써 뜯어내고 2D에 Projection 시켜 3D의 영상을 2D 형식으로 변환하고 2D로 변환된 Point Cloud 영상을 기존의 2D 압축 코덱을 활용하여 압축하는 기술이다. 이 V-PCC로 변환된 2D 영상은 기존 2D 영상을 전송하는 방식을 활용하여 네트워크 기반 전송이 가능하다. 본 논문에서는 V-PCC 방식으로 압축한 V3C 데이터를 방송망으로 전송 및 소비하기 위해 MPEG Media Transport(MMT) Packet을 만드는 패킷화 방안을 제안한다. 또한 Server와 Client에서 주고받은 V3C(Visual Volumetric Video Coding) 데이터의 비트스트림을 비교하여 검증한다.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.553-565
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• 2021

Point cloud is a set of points for representing a 3D object, and consists of geometric information, which is 3D coordinate information, and attribute information, which is information representing color, reflectance, and the like. In this way of expressing, it has a vast amount of data compared to 2D images. Therefore, a process of compressing the point cloud data in order to transmit the point cloud data or use it in various fields is required. Unlike color information corresponding to all 2D geometric information constituting a 2D image, a point cloud represents a point cloud including attribute information such as color in only a part of the 3D space. Therefore, separate processing of geometric information is also required. Based on these characteristics of point clouds, MPEG under ISO/IEC standardizes V-PCC, which imitates point cloud images and compresses them into 2D DCT-based 2D image compression codecs, as a compression method for high-density point cloud data. This has limitations in accurately representing 3D spatial information to proceed with compression by converting 3D point clouds to 2D, and difficulty in processing non-existent points when utilizing 3D DCT. Therefore, in this paper, we present 3D Discrete Cosine Transform-based Point Cloud Compression (3DCT PCC), a method to compress point cloud data, which is a 3D image by utilizing 3D DCT, and confirm the efficiency of 3D DCT compared to V-PCC based on 2D DCT.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• Annual Conference of KIPS
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• 2020.05a
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• pp.532-533
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• 2020

원격 및 가상환경에서 사용자의 동작에 따른 3D 모델을 제공하기 위해 light detection and range (LiDAR)로 측정된 3D point cloud로 사용자의 3D 모델이 생성되어 원격 및 가상환경에 사용자의 모습이 제공된다. 하지만 3D 모델을 생성하기 위해서는 사용자의 신체 전부가 측정된 3D point cloud가 필요하다. 사용자의 신체 전체를 측정하기 위해서는 적어도 두 개 이상의 LiDAR가 필요하다. 두 개 이상의 LiDAR을 사용할 경우에는 LiDAR을 사용할 공간과 LiDAR를 구비하기 위한 비용이 발생한다. 단일 LiDAR로 3D 모델을 생성하는 방법이 요구된다. 본 논문에서는 단일 LiDAR에서 측정된 3D point cloud를 이용하여 3D 모델을 생성하는 방법이 제안된다. End-to-End 기반 Convolutional Neural Network (CNN) 모델로 측정된 3D point cloud를 분석하여 사용자의 체형과 자세를 예측하도록 학습한다. 기본자세를 취하는 동안 수집된 3D point cloud로 기본이 되는 사용자의 3D 모델을 생성한다. 학습된 CNN 모델을 통하여 측정된 3D point cloud로 사용자의 자세를 예측하여 기본이 되는 3D 모델을 수정하여 3D 모델을 제공한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.84-90
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• 2014

This paper presents the results of three-dimensional face point cloud smoothing based on a modified anisotropic diffusion method. The focus of this research was to obtain a 3D face point cloud with a smooth texture and number of vertices equal to the number of vertices input during the smoothing process. Different from other methods, such as using a template D face model, modified anisotropic diffusion only uses basic concepts of convolution and filtering which do not require a complex process. In this research, we used 6D point cloud face data where the first 3D point cloud contained data pertaining to noisy x-, y-, and z-coordinate information, and the other 3D point cloud contained data regarding the red, green, and blue pixel layers as an input system. We used vertex selection to modify the original anisotropic diffusion. The results show that our method has improved performance relative to the original anisotropic diffusion method.

• Journal of KIBIM
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• v.11 no.4
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• pp.1-9
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• 2021

Comparing to general scanning data, the 3D digital map for large construction sites and complex buildings consists of millions of points. The large construction site needs to be scanned multiple times by drone photogrammetry or terrestrial laser scanner (TLS) survey. The scanned point cloud data are required to be registrated with high resolution and high point density. Unlike the registration of 2D data, the matrix of translation and rotation are used for registration of 3D point cloud data. Archiving high accuracy with 3D point cloud data is not easy due to 3D Cartesian coordinate system. Therefore, in this study, iterative closest point (ICP) registration method for improve accuracy of 3D digital map was employed by different overlap ratio on 3D digital maps. This study conducted the accuracy test using different overlap ratios of two digital maps from 10% to 100%. The results of the accuracy test presented the optimal overlap ratios for an ICP registration method on digital maps.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.107-111
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• 2021

A 3D point cloud map is an essential elements in various fields, including precise autonomous navigation system. However, generating a 3D point cloud map using a single sensor has limitations due to the price of expensive sensor. In order to solve this problem, we propose a precise 3D mapping system using low-cost sensor fusion. Generating a point cloud map requires the process of estimating the current position and attitude, and describing the surrounding environment. In this paper, we utilized a commercial visual-inertial odometry sensor to estimate the current position and attitude states. Based on the state value, the 2D LiDAR measurement values describe the surrounding environment to create a point cloud map. To analyze the performance of the proposed algorithm, we compared the performance of the proposed algorithm and the 3D LiDAR-based SLAM (simultaneous localization and mapping) algorithm. As a result, it was confirmed that a precise 3D point cloud map can be generated with the low-cost sensor fusion system proposed in this paper.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.35-41
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• 2020

3D laser scanners are an effective way to quickly acquire a large amount of data about an object. Recently, it is used in various fields such as surveying, displacement measurement, 3D data generation of objects, construction of indoor spatial information, and BIM(Building Information Model). In order to utilize the point cloud data acquired through the 3D laser scanner, it is necessary to make the data acquired from many stations through a matching process into one data with a unified coordinate system. However, analytical researches on the accuracy of point cloud data according to the registration method are insufficient. In this study, we tried to analyze the accuracy of registration method of point cloud data acquired through 3D laser scanner. The point cloud data of the study area was acquired by 3D laser scanner, the point cloud data was registered by the ICP(Iterative Closest Point) method and the shape registration method through the data processing, and the accuracy was analyzed by comparing with the total station survey results. As a result of the accuracy evaluation, the ICP and the shape registration method showed 0.002m~0.005m and 0.002m~0.009m difference with the total station performance, respectively, and each registration method showed a deviation of less than 0.01m. Each registration method showed less than 0.01m of variation in the experimental results, which satisfies the 1: 1,000 digital accuracy and it is suggested that the registration of point cloud data using ICP and shape matching can be utilized for constructing spatial information. In the future, matching of point cloud data by shape registration method will contribute to productivity improvement by reducing target installation in the process of building spatial information using 3D laser scanner.