• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.53-61
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• 2006

In this paper, we propose a novel method for 3D reconstruction of an indoor scene using a multi-view camera. Until now, numerous disparity estimation algorithms have been developed with their own pros and cons. Thus, we may be given various sorts of depth images. In this paper, we deal with the generation of a 3D surface using several 3D point clouds acquired from a generic multi-view camera. Firstly, a 3D point cloud is estimated based on spatio-temporal property of several 3D point clouds. Secondly, the evaluated 3D point clouds, acquired from two viewpoints, are projected onto the same image plane to find correspondences, and registration is conducted through minimizing errors. Finally, a surface is created by fine-tuning 3D coordinates of point clouds, acquired from several viewpoints. The proposed method reduces the computational complexity by searching for corresponding points in 2D image plane, and is carried out effectively even if the precision of 3D point cloud is relatively low by exploiting the correlation with the neighborhood. Furthermore, it is possible to reconstruct an indoor environment by depth and color images on several position by using the multi-view camera. The reconstructed model can be adopted for interaction with as well as navigation in a virtual environment, and Mediated Reality (MR) applications.

• Tunnel and Underground Space
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• v.17 no.3 s.68
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• pp.165-174
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• 2007

The technique, which reproduces the figures of objects from measured data of the objects using 3-D laser scanner, is called reverse engineering. Recently, research studies into applications of reverse engineering to rock engineering are increasing in number, in the discontinuity surveys for rock slopes out of man's reach, or rapid discontinuity surveys for wide range areas. For analysis of discontinuity using reverse engineering, a program for processing point clouds data from the 3-D laser scanner, for sampling from these point clouds data, and finally analyzing the discontinuity is needed. However, existing programs rarely have sufficient functions to properly analyze the discontinuities. In this study, a program was developed, which can automatically sample discontinuities from the point clouds data which measured in a rock slope using a 3-D laser scanner, and which can also undertake statistical analysis of the discontinuities. This developed program was verified by the application of discontinuity surveys in a rock slope and a tunnel. By undertaking the discontinuity survey using a 3-D laser scanner and the developed program, the feasibility and rapidity of such surveys is expected to improve in areas out of man's reach in geotechnical surveys. Taking into consideration the fact that the international level of related techniques is at a rudimentary stage, the possibility of prior occupation of a broad market is also expected.

• 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 KIBIM
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• v.14 no.2
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• pp.1-12
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• 2024

The paper proposes a methodology that combines the Random Sample Consensus (RANSAC) algorithm and the Point Cloud Encoder-Decoder Network (PCEDNet) algorithm to automatically extract the length of infrastructure elements from point cloud data acquired through 3D LiDAR scans of retaining walls. This methodology is expected to significantly improve time and cost efficiency compared to traditional manual measurement techniques, which are crucial for the data-driven analysis required in the precision-demanding construction sector. Additionally, the extracted positional and dimensional data can contribute to enhanced accuracy and reliability in Scan-to-BIM processes. The results of this study are anticipated to provide important insights that could accelerate the digital transformation of the construction industry. This paper provides empirical data on how the integration of digital technologies can enhance efficiency and accuracy in the construction industry, and offers directions for future research and application.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.545-554
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• 2019

This research aims to provide a building area extraction approach over the areas where data acquisition is impossible through field surveying, aerial photography and lidar scanning. Hence, high-resolution satellite images, which have high accessibility over the earth, are utilized for the automated building extraction in this study. 3D point clouds or DSM (Digital Surface Models), derived from the stereo image matching process, provides low quality of building area extraction due to their high level of noises and holes. In this regards, this research proposes a hybrid building area extraction approach which utilizes 3D point clouds (from image matching), and color and linear information (from imagery). First of all, ground and non-ground points are separated from 3D point clouds; then, the initial building hypothesis is extracted from the non-ground points. Secondly, color based building hypothesis is produced by considering the overlapping between the initial building hypothesis and the color segmentation result. Afterwards, line detection and space partitioning results are utilized to acquire the final building areas. The proposed approach shows 98.44% of correctness, 95.05% of completeness, and 1.05m of positional accuracy. Moreover, we see the possibility that the irregular shapes of building areas can be extracted through the proposed approach.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.451-456
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• 2018

UAVs (Unmanned Aerial Vehicles) are widely used for maintenance and monitoring of facilities. It is necessary to acquire a high-resolution image for evaluating the appearance state of the facility in safety inspection. In addition, it is essential to acquire the video data in order to acquire data over a wide area rapidly. In general, since video data does not include position information, it is difficult to analyze the actual size of the inspection object quantitatively. In this study, we evaluated the utilization of 3D point cloud data of bridges using a matching between video frames and reference photos. The drones were used to acquire video and photographs. And exterior orientations of the video frames were generated through feature point matching with reference photos. Experimental results showed that the accuracy of the video frame data is similar to that of the reference photos. Furthermore, the point cloud data generated by using video frames represented the shape and size of bridges with usable accuracy. If the stability of the product is verified through the matching test of various conditions in the future, it is expected that the video-based facility modeling and inspection will be effectively conducted.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.5
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• pp.375-381
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• 2007

A novel method to register images onto 3D data, such as 3D point cloud, 3D vectors, and 3D surfaces, is proposed. The proposed method estimates the exterior orientation of a camera with respective to the 3D data though fitting pairs of the normal vectors of two planes passing a focal point and 2D and 3D lines extracted from an image and the 3D data, respectively. The fitting condition is that the angle between each pair of the normal vectors has to be zero. This condition can be represented as a numerical formula using the inner product of the normal vectors. This paper demonstrates the proposed method can estimate the exterior orientation for the image registration as simulation tests.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.239-245
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• 2020

Recently, BIM (Building Information Modeling) are widely being utilized in Construction industry. However, most structures that have been constructed in the past do not have BIM. For structures without BIM, the use of SfM (Structure from Motion) techniques in the 2D image obtained from the camera allows the generation of 3D model point cloud data and BIM to be established. However, since these generated point cloud data do not contain semantic information, it is necessary to manually classify what elements of the structure. Therefore, in this study, deep learning was applied to automate the process of classifying structural components. In the establishment of deep learning network, Inception-ResNet-v2 of CNN (Convolutional Neural Network) structure was used, and the components of bridge structure were learned through transfer learning. As a result of classifying components using the data collected to verify the developed system, the components of the bridge were classified with an accuracy of 96.13 %.

• Proceedings of Korean Society of Archives and Records Management
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• 2019.05a
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• pp.43-47
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• 2019

In the field of cultural heritage, for the purpose of its preservation and restoration, cultural heritage has been recorded using three-dimensional (3D) digital technology. However, these 3D digital records are a large-scale digital record produced through complicated steps from point cloud data acquisition to 3D model production, and long-term preservation problems cannot be avoided. With international discussions are ongoing, this study looks into the preservation direction of the cultural heritage 3D digital records based on precedent research and case analysis.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.9
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• pp.401-410
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• 2016

3D registration is a computer vision technique of aligning multi-view range images with respect to a reference coordinate system. Various 3D registration algorithms have been introduced in the past few decades. Iterative Closest Point (ICP) is one of the widely used 3D registration algorithms, where various modifications are available nowadays. In the ICP-based algorithms, the closest points are considered as the corresponding points. However, this assumption fails to find matching points accurately when the initial pose between point clouds is not sufficiently close. In this paper, we propose a new method to solve this problem using the 6D distance (3D color space and 3D Euclidean distances). Moreover, a color segmentation-based adaptive sampling technique is used to reduce the computational time and improve the registration accuracy. Several experiments are performed to evaluate the proposed method. Experimental results show that the proposed method yields better performance compared to the conventional methods.