• Journal of Information Processing Systems
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• v.13 no.3
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• pp.573-589
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• 2017

Cloud computing is an attractive solution that can provide low cost storage and powerful processing capabilities for government agencies or enterprises of small and medium size. Yet the confidentiality of information should be considered by any organization migrating to cloud, which makes the research on relational database system based on encryption schemes to preserve the integrity and confidentiality of data in cloud be an interesting subject. So far there have been various solutions for realizing SQL queries on encrypted data in cloud without decryption in advance, where generally homomorphic encryption algorithm is applied to support queries with aggregate functions or numerical computation. But the existing homomorphic encryption algorithms cannot encrypt floating-point numbers. So in this paper, we present a mechanism to enable the trusted party to encrypt the floating-points by homomorphic encryption algorithm and partial trusty server to perform summation on their ciphertexts without revealing the data itself. In the first step, we encode floating-point numbers to hide the decimal points and the positive or negative signs. Then, the codes of floating-point numbers are encrypted by homomorphic encryption algorithm and stored as sequences in cloud. Finally, we use the data structure of DoubleListTree to implement the aggregate function of SUM and later do some extra processes to accomplish the summation.

• 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 Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.387-388
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• 2010

There is no generalized and systematic method yet to data pre-processing for point cloud data classification even if there have been lots of previous studies such as local maxima filter, morphology filter, slope based filter and so on. Main focus of this study is to present classification method for bare ground information from LiDAR data for the mountainous area.

• 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 the Korean Society for Precision Engineering
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• v.23 no.9 s.186
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• pp.200-207
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• 2006

In this paper a new approach which combines implicit surface scheme and NURBS surface interpolation method is proposed in order to generate a complete surface model from unorganized point cloud data. In the method a base surface was generated by creating smooth implicit surface from the input point cloud data through which the actual surface would pass. The implicit surface was defined by a combination of shape functions including quadratic polynomial function, cubic polynomial functions and radial basis function using adaptive domain decomposition method. In this paper voxel data which can be extracted easily from the base implicit surface were used in order to generate rectangular net with good quality using the normal projection and smoothing scheme. After generating the interior points and tangential vectors in each rectangular region considering the required accuracy, the NURBS surface were constructed by interpolating the rectangular array of points using boundary tangential vectors which assure C$^1$ continuity between rectangular patches. The validity and effectiveness of this new approach was demonstrated by performing numerical experiments for the various types of point cloud data.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.3-12
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• 2020

Nowadays, as-is BIM generation has been popularly adopted in the architecture, engineering, construction and facility management (AEC/FM) industries. In order to generate a 3D as-is BIM of a structural component, current methods require a registration process that merges different sets of point cloud data obtained from multiple locations, which is time-consuming and registration error-prone. To tackle this limitation, this study proposes a registration-free 3D point cloud data acquisition technique for as-is BIM generation. In this study, small-size mirrors that rotate in both horizontal and vertical direction are used to enable the registration-free data acquisition technique. First, a geometric model that defines the relationship among the mirrors, the laser scanner and the target component is developed. Second, determinations of optimal laser scanner location and mirror location are performed based on the developed geometrical model. To validate the proposed registration-free as-is BIM generation technique, simulation tests are conducted on key construction components including a PC slab and a structural wall. The result demonstrates that the registration-free point cloud data acquisition technique can be applicable in various construction elements including PC elements and structural components for as-is BIM generation.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.99-106
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• 2019

Technology that cannot be excluded from 4th industry is self-driving sector. The self-driving sector can be seen as a key set of technologies in the fourth industry, especially in the DB sector is getting more and more popular as a business. The DB, which was previously produced and managed in two dimensions, is now evolving into three dimensions. Among the data obtained by Mobile Mapping System () to produce the HD MAP necessary for self-driving, Point Cloud, which is LiDAR data, is used as a DB because it contains accurate location information. However, at present, it is not widely used as a base data for 3D modeling in addition to HD MAP production. In this study, MMS Point Cloud was used to extract facilities around the road and to overlay the location to expand the usability of Point Cloud. Building utility poles and communication poles DB from Point Cloud and comparing road name address base and location, it is believed that the accuracy of the location of the facility DB extracted from Point Cloud is also higher than the basic road name address of the road, It is necessary to study the expansion of the facility field sufficiently.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.5
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• pp.330-337
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• 2022

Research on object detection algorithms using 2D data has already progressed to the level of commercialization and is being applied to various manufacturing industries. Object detection technology using 2D data has an effective advantage, there are technical limitations to accurate data generation and analysis. Since 2D data is two-axis data without a sense of depth, ambiguity arises when approached from a practical point of view. Advanced countries such as the United States are leading 3D data collection and research using 3D laser scanners. Existing processing and detection algorithms such as ICP and RANSAC show high accuracy, but are used as a processing speed problem in the processing of large-scale point cloud data. In this study, PointNet a representative technique for detecting objects using widely used 3D point cloud data is analyzed and described. And RandLA-Net, which overcomes the limitations of PointNet's performance and object prediction accuracy, is described a review of detection technology using point cloud data was conducted.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1482-1484
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• 2019

We present a method for transmitting 3D object information in real time in a telepresence system. Three-dimensional object information consists of a large amount of point cloud data, which requires high performance computing power and ultra-wideband network transmission environment to process and transmit such a large amount of data in real time. In this paper, multiple users can transmit object motion and facial expression information in real time even in small network bands by using GANs (Generative Adversarial Networks), a non-supervised learning machine learning algorithm, for real-time transmission of 3D point cloud data. In particular, we propose the creation of an object similar to the original using only the feature information of 3D objects using conditional GANs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1452-1455
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• 2021

Recently, point clouds are generated by capturing real space in 3D, and it is actively applied and serviced for performances, exhibitions, education, and training. These point cloud data require post-correction work to be used in virtual environments due to errors caused by the capture environment with sensors and cameras. In this paper, we propose an enhancement technique for 3D point cloud data by applying generative adversarial network(GAN). Thus, we performed an approach to regenerate point clouds as an input of GAN. Through our method presented in this paper, point clouds with a lot of noise is configured in the same shape as the real object and environment, enabling precise interaction with the reconstructed content.