• 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 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.

• 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.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1283-1287
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• 2003

Reverse engineering technology refers to the process that creates a CAD model of an existing part using measuring devices. Recently, non-contact scanning devices have become more accurate and the speed of data acquisition has increased drastically. However, they generate thousands of points per second and various types of point data. Therefore. it becomes a important to handle the huge amount and various types of point data to generate a surface model efficiently. This paper proposes a new triangular mesh generation method using 3D grids. The geometric information of a part can be obtained from point cloud data by estimating normal values of the points. In our research, the non-uniform 3D grids are generated first for feature based data reduction based on the geometric information. Then, triangulation is performed with the reduced point data. The grid structure is efficiently used not only for neighbor point search that can speed up the mesh generation process but also for getting surface connectivity information to result in same topology surface with the point data. Through this integrated approach, it is possible to create surface models from scanned point data efficiently.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.10
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• pp.928-935
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• 2013

In this paper, real-time polygon generation algorithm of 3D point cloud data and texture mapping for tele-operation is proposed. In a tele-operation, it is essential to provide more highly realistic visual information to a tele-operator. By using 3D point cloud data, the tele-operator can observe the working environment from various view point with a reconstructed 3D environment. However, there are huge empty space in 3D point cloud data, since there is no environmental information among the points. This empty space is not suitable for an environmental information. Therefore, real-time polygon generation algorithm of 3D point cloud data and texture mapping is presented to provide more highly realistic visual information to the tele-operator. The 3D environment reconstructed from the 3D point cloud data with texture mapped polygons is the crucial part of the tele-operation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.435-438
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• 2001

Reverse engineering technology refers to the process that creates a CAD model of an existing part using measuring devices. Recently, non-contact scanning devices have become more accurate and the speed of data acquisition has increased drastically. However, they generate thousands of points per second and various types of point data. Therefore, it becomes a major issue to handle the huge amount and various types of point data. To generate a CAD model from scanned point data efficiently, these point data should be well arranged through point data handling processes such as data reduction and segmentation. This paper proposes a new point data handling method using 3D grids. The geometric information of a part is extracted from point cloud data by estimating normal values of the points. The non-uniform 3D grids for data reduction and segmentation are generated based on the geometric information. Through these data reduction and segmentation processes, it is possible to create CAD models autmatically and efficiently. The proposed method is applied to two quardric medels and the results are discussed.

• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.21-26
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• 2018

Recently, the emergence of 3D cameras, 3D scanners and various cameras including Lidar is expected to be applied to applications such as AR, VR, and autonomous mobile vehicles that deal with 3D data. In Particular, the 3D point cloud data consisting of tens to hundreds of thousands of 3D points is rapidly increased in capacity compared with 2D data, Efficient encoding / decoding technology for smooth service within a limited bandwidth, and efficient service provision technology for differentiating the area of interest and the surrounding area are needed. In this paper, we propose a new quality parameter considering characteristics of 3D point cloud instead of quality change based on assumed video codec in MPEG V-PCC used in 3D point cloud compression, 3D Grid division method and representation for selectively transmitting 3D point clouds according to user's area of interest, and propose a new 3D Manifesto. By using the proposed technique, it is possible to generate more bitrate images, and it is confirmed that the efficiency of network, decoder, and renderer can be increased while selectively transmitting as needed.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.9
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• pp.700-706
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• 2003

This paper presents a 3D last design system that provides the 3-dimensional last data based on the FFD(Free Form Deformation) method. The proposed system utilizes the control points for deformation factor to convert from the 3D point cloud foot data to the 3D point cloud last data. The deformation factor of the FFD is obtained from the conventional last design technique, and constructed on the FFD lattice based on the bottom view and lateral view of the measured 3D point cloud foot data. In addition, the control points of FFD lattice is decided on the anatomical points of foot. The deformed 3D last obtained from the proposed FFD is saved as a 3D dxf foot data. The experimental results demonstrate that the proposed system have the descent 3D last data based on the openGL window.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1100-1105
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• 2022

Denoising, registering, and detecting changes of 3D digital map are generally conducted by skilled technicians, which leads to inefficiency and the intervention of individual judgment. The manual post-processing for analyzing 3D point cloud data of construction sites requires a long time and sufficient resources. This study develops automation technology for analyzing 3D point cloud data for construction sites. Scanned data are automatically denoised, and the denoised data are stored in a specific storage. The stored data set is automatically registrated when the data set to be registrated is prepared. In addition, regions with non-homogeneous densities will be converted into homogeneous data. The change detection function is developed to automatically analyze the degree of terrain change occurred between time series data.