• Spatial Information Research
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• v.12 no.2
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• pp.137-149
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• 2004

For 3D modelling artificial objects with computers, we have to draw frames and paint the facet images on each side. In this paper, a geographic modelling system building automatically 3D geographic spaces using GIS data and real images of buildings is proposed. First, the 3D model of terrain is constructed by using TIN and DEM algorithms. The images of buildings are acquired with a camera and its position is estimated using vertical lines of the image and the GIS data. The height of the building is computed with the image and the position of the camera, which used for making up the frames of buildings. The 3D model of the building is obtained by detecting the facet iamges of the building and texture mapping them on the 3D frame. The proposed geographical modeling system is applied to real area and shows its effectiveness.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.484-487
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• 2010

본 논문에서는 BIM(Building Information Modeling)의 핵심기술인 파라메트릭 기술을 기반으로 하여 철근콘크리트 구조물의 기둥부재 주철근 자동배근시스템을 구축함으로써 기존 프로그램에서 사용자가 직접 입력해야하는 변수의 수를 최소화하고 사용성과 정확성을 높이는 것을 목적으로 한다. 기존 철근배근 형상 자동 모델링에서 기둥철근의 자동 모델링은 기둥단면이 변하는 부분에서의 철근 배근과 정착 및 이음길이를 고려하지 않고 있다. 만약 고려하더라도 이용자가 직접 입력하는 방식이기 때문에 규모가 큰 건물일 경우 방대한 정보의 처리 미숙으로 인해 정확한 모델링을 기대하기 어려운 실정이다. 본 연구에서는 기둥 부재에 대하여 대상 건물을 선정하고 구조해석 모델링을 구축한 후 구조해석 결과 데이터베이스를 추출하여 얻은 정보와 건축구조설계기준에 따른 정착 및 이음 길이 산정에 관한 알고리즘을 구축하여 철근배근 형상 자동화 모듈에 적용하여 배근 자동 설계 및 자동 형상화 모듈을 생성하였다.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.4
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• pp.117-125
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• 2012

Although many studies have been conducted to extract buildings from airborne lidar data, most of them assume that all the boundaries of a building are straight line segments. This makes it difficult to model building boundaries containing curved segments correctly. This paper aims to model buildings containing curved segments as combination of straight lines and arcs. First, two sets of boundary points are extracted by adaptive convex hull algorithm and local convex hull algorithm with a larger radius. Then, arc segments are determined by average spacing of boundary points and intersection ratio of perpendicular lines. Finally, building boundary is modeled through regularization of least squares line or circle fitting. The experimental results showed that the proposed method can model the curved building boundaries as arc segments correctly by completeness of 69% and correctness of 100%. The approach will be utilized effectively to create automatically digital map that meets the conditions of the Korean digital mapping.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1217-1232
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• 2009

In this paper, we propose a semi-automatic 3D building reconstruction method using uncalibrated images which includes the facade of target building. First, we extract feature points in all images and find corresponding points between each pair of images. Second, we extract lines on each image and estimate the vanishing points. Extracted lines are grouped with respect to their corresponding vanishing points. The adjacency graph is used to organize the image sequence based on the number of corresponding points between image pairs and camera calibration is performed. The initial solid model can be generated by some user interactions using grouped lines and camera pose information. From initial solid model, a detailed building model is reconstructed by a combination of predefined basic Euler operators on half-edge data structure. Automatically computed geometric information is visualized to help user's interaction during the detail modeling process. The proposed system allow the user to get a 3D building model with less user interaction by augmenting various automatically generated geometric information.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.549-554
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• 2008

도시와 같은 광범위한 영역에 대한 지형, 지물의 기하 정보를 추출 또는 복원하는 기술은 공간 영상정보 시스템을 비롯한 다양한 응용분야에서 사용되고 있으며, 이러한 필요에 따라 중요성이 더욱 커지고 있다. 본 논문은 위성 영상에서 건물의 footprint와 rooftop, 그림자 정보를 이용하여 건물을 반 자동으로 모델링하고, 이를 지형에 정합하는 시스템을 제안한다. 제안하는 시스템은 사용자의 직접적인 조작과 자동으로 이루어는 부분을 조합하여, 최소한의 사용자 조작으로 건물을 모델링하고, 지형에 의한 요소를 고려하여 건물의 실제 위치를 보정하여 지형과 정합을 수행한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.619-628
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• 2009

LiDAR has capability of rapid data acquisition and provides useful information for reconstructing surface of the Earth. However, Extracting information from LiDAR data is not easy task because LiDAR data consist of irregularly distributed point clouds of 3D coordinates and lack of semantic and visual information. This thesis proposed methods for automatic extraction of buildings and 3D detail modeling using airborne LiDAR data. As for preprocessing, noise and unnecessary data were removed by iterative surface fitting and then classification of ground and non-ground data was performed by analyzing histogram. Footprints of the buildings were extracted by tracing points on the building boundaries. The refined footprints were obtained by regularization based on the building hypothesis. The accuracy of building footprints were evaluated by comparing with 1:1,000 digital vector maps. The horizontal RMSE was 0.56m for test areas. Finally, a method of 3D modeling of roof superstructure was developed. Statistical and geometric information of the LiDAR data on building roof were analyzed to segment data and to determine roof shape. The superstructures on the roof were modeled by 3D analytical functions that were derived by least square method. The accuracy of the 3D modeling was estimated using simulation data. The RMSEs were 0.91m, 1.43m, 1.85m and 1.97m for flat, sloped, arch and dome shapes, respectively. The methods developed in study show that the automation of 3D building modeling process was effectively performed.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.535-540
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• 2006

This paper presented an effective strategy to extract the buildings and to reconstruct 3-D buildings using high-resolution multispectral stereo satellite images. Proposed scheme contained three major steps: building enhancement and segmentation using both BDT (Background Discriminant Transformation) and ISODATA algorithm, conjugate building identification using the object matching with Hausdorff distance and color indexing, and 3-D building reconstruction using photogrammetric techniques. IKONOS multispectral stereo images were used to evaluate the scheme. As a result, the BDT technique was verified as an effective tool for enhancing building areas since BDT suppressed the dominance of background to enhance the building as a non-background. In building recognition, color information itself was not enough to identify the conjugate building pairs since most buildings are composed of similar materials such as concrete. When both Hausdorff distance for edge information and color indexing for color information were combined, most segmented buildings in the stereo images were correctly identified. Finally, 3-D building models were successfully generated using the space intersection by the forward RFM (Rational Function Model).

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.139-140
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• 2008

지상라이다는 고정도의 3차원 영상을 제공하고 레이저 빔을 현장이나 대상물에 발사하여 짧은 시간에 수백만점의 3차원좌표를 기록할 수 있는 최신 측량장비로서 다양한 응용분야에서 두각을 나타내고 있다. 본 연구에서는 지상라이다를 이용한 건축물의 3자윈 자동 윤곽선 추출을 다룬다. 지상라이다는 건축물의 3차원 윤곽선을 신속하게 추출할 수 있지만 지상기반 시스템이므로 여러 가지 장애물 때문에 건국물의 하단부에서는 추출이 쉽지 않다. 기존 항공라이다를 이용한 알고리즘에서는 사진의 색상차나 모폴로지 특성에 의존하여 범위를 제한하고, 이를 기반으로 윤곽선을 추출하였다. 하지만 지상라이다의 경우 항공라이다에 비해 분해능이 월등히 높다. 또한, 지상라이다는 지상에서 측정하기 때문에 항공라이다에서 어려운 건축물의 측면이나 정면도 윤곽선 추출이 가능하기 때문에 본 논문에서는 사진을 이용하지 않고 전처리를 하지 않은 데이터를 직접 이용하여 건물의 정면 윤곽선을 추출하는 것을 제안한다. 건물의 크기와 데이터 수 즉, 라이다로 측정한 포인트 수를 고려한 효율적인 Decimation방법을 제안하고 또한, Decimation된 데이터이서 지역적으로 제일 큰 값과 작은 값을 찾아낸다. 그 중 많이 벗어난 점을 편차를 이용하여 제거한다. 이렇게 찾아낸 건축물의 외곽점들을 이어 만든 윤곽선을 최종적으로 보간하여 좀 더 현실과 가까운 윤곽선 추출 방법을 제안한다.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.973-985
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• 2019

In this paper, we propose a method to separate the ground and building areas and generate building models automatically through planarity analysis using UAV (Unmanned Aerial Vehicle) based point cloud. In this study, proposed method includes five steps. In the first step, the planes of the point cloud were extracted by analyzing the planarity of the input point cloud. In the second step, the extracted planes were analyzed to find a plane corresponding to the ground surface. Then, the points corresponding to the plane were removed from the point cloud. In the third step, we generate ortho-projected image from the point cloud ground surface removed. In the fourth step, the outline of each object was extracted from the ortho-projected image. Then, the non-building area was removed using the area, area / length ratio. Finally, the building's outer points were constructed using the building's ground height and the building's height. Then, 3D building models were created. In order to verify the proposed method, we used point clouds made using the UAV images. Through experiments, we confirmed that the 3D models of the building were generated automatically.