• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.25 no.1
• /
• pp.19-30
• /
• 2007

Modeling 3D buildings is an essential process to revive the real world into a computer. There are two ways to create a 3D building model. The first method is to use the building layer of 1:1000 digital maps based on high density point data gained from airborne laser surveying. The second method is to use LiDAR point data with digital images achieved with LiDAR. In this research we tested one sheet area of 1:1000 digital map with both methods to process a 3D building model. We have developed a process, analyzed quantitatively and evaluated the efficiency, accuracy, and reality. The resulted differed depending on the buildings shape. The first method was effective on simple buildings, and the second method was effective on complicated buildings. Also, we evaluated the accuracy of the produced model. Comparing the 3D building based on LiDAR data and digital image with digital maps, the horizontal accuracy was within ${\pm}50cm$. From the above we derived a conclusion that 3D building modeling is more effective when it is based on LiDAR data and digital maps. Using produced 3D building modeling data, we will be utilized as digital contents in various fields like 3D GIS, U-City, telematics, navigation, virtual reality and games etc.

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

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.4
• /
• pp.353-362
• /
• 2012

3D building modeling from airborne Lidar without model regularization may cause positional errors or topological inconsistency in building models. Regularization of 3D building models, on the other hand, restricts the types of models which can be reconstructed. To resolve these issues, this paper modelled 3D buildings from airborne Lidar by building model regularization which considers more various types of buildings. Building points are first segmented into roof planes by clustering in feature space and segmentation in object space. Then, 3D building models are reconstructed by consecutive adjustment of planes, lines, and points to satisfy parallelism, symmetry, and consistency between model components. The experimental results demonstrated that the method could make more various types of 3d building models with regularity. The effects of regularization on the positional accuracies of models were also analyzed quantitatively.

• Korean Journal of Remote Sensing
• /
• v.24 no.2
• /
• pp.141-152
• /
• 2008

The 3D building modeling is one of crucial components in constructing 3D geospatial information. The existing methods for 3D building modeling depend mainly on manual photogrammetric processes, which indeed take great amount of time and efforts. In recent years, many researches on 3D building modeling using aerial LiDAR data have been actively performed to aim at overcoming the limitations of existing 3D building modeling methods. Either techniques with interpolated grid data or data fusion with digital map and images have been investigated in most of existing researches on 3D building modeling with aerial LiDAR data. The paper proposed a method of 3D building modeling with LiDAR data only. Firstly, octree-based segmentation is applied recursively to LiDAR data classified as buildings in 3D space until there are no more LiDAR points to be segmented. Once octree-based segmentation is completed, each segmented patch is thereafter merged together based on its geometric spatial characteristics. Secondly, building model components are created with merged patches. Finally, a 3D building model is generated and composed with building model components. The experimental results with real LiDAR data showed that the proposed method was capable of modeling various types of 3D buildings.

• Journal of Korean Society for Geospatial Information Science
• /
• v.13 no.3 s.33
• /
• pp.25-32
• /
• 2005

This paper presents a method for point-based 3D building reconstruction using Lidar data and digital map. The proposed method consists of three processes: extraction of building roof points, identification of roof types, and 3D building reconstruction. After extracting points inside the polygon of building, the ground surface, wall and tree points among the extracted points are removed through the filtering process. The filtered points are then fitted into the flat plane using ODR(Orthogonal Distance Regression) in the first place. If the fitting error is within the predefined threshold, the surface is classified as a flat roof. Otherwise, the surface is fitted and classified into a gable or arch roof through RMSE analysis. Experimental results showed that the proposed method classified successfully three different types of roof and that the fusion of LIDAR data and digital map could be a feasible method of modeling 3D building reconstruction.

• Proceedings of the KAIS Fall Conference
• /
• 2010.11a
• /
• pp.32-35
• /
• 2010

바람직한 도시의 건설계획을 위해 2차원 적인 도면의 활용은 한계가 있다. 지방자치단체의 도시계획심의와 민원의 처리에도 3차원 도시모델을 활용하는 추세에 있다. 현장에 가지 않고 사무실에서 업무를 처리하기 위해서는 정밀한 3차원 지형모델이 구축되어야 한다. 또한 건물의 3차원모델링도 신속하고 편리한 방법을 이용해야만 실무에 활용이 가능하다. 본 연구에서는 이를 위해 고해상 고정밀 3차원 지형모델링은 물론 스케치업을 이용한 효율적인 건물모델링방법 강구하여 실무에 적용가능성을 제시하였다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
• /
• 2003.04a
• /
• pp.550-555
• /
• 2003

최근에 Lidar 데이터를 이용한 3차원 위치 정보와 지표면 속성 정보를 취득하는 연구가 많이 진행되고 있다. 높은 위치 정확도, 3차원 데이터 동시 취득, 기존 측정 방식에 비하여 점 데이터 취득의 자동화, 데이터 정확도의 안정성 등으로 인하여 복잡한 지형 및 인공구조물이 존재하는 지역에서 Lidar 데이터의 응용 사례가 많이 나타나고 있으며, 특히 건물 모델링에서 반자동 방식의 디지털 사진측량에 비하여 자동 모델링의 가능성을 보여주고 있다. 일반적으로 Lidar 데이터의 점밀도는 1점/㎡이내이며, 촬영된 스트립을 중복시켜 점밀도를 높이기도 한다. 건물은 크기와 형태가 다양하기 때문에 모델링에 필요한 점밀도를 제시하기는 어렵지만 5점 내외에서 모델링이 가능하다고 알려져 있으며 건물이외에 다른 지형지물에 대한 모델링 연구는 거의 이루어지지 않고 있다. 따라서 본 논문에서는 Lidar 데이터의 점밀도에 따라 지물의 모델링 가능성을 평가하고 효율적인 데이터 취득 방안을 제시하고자 한다.

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

• 한국공간정보시스템학회:학술대회논문집
• /
• 2005.05a
• /
• pp.453-457
• /
• 2005

3차원 지형공간정보체계에 대한 관심의 증가와 함께 도심지의 3차원 모델링에 관한 다양한 연구가 활발히 진행되고 있다. 단색영상을 용하여 영역기반정합이나 형상기반정합을 실시하던 기존의 3차원 모델링 기법은 오정합이 많이 발생할 수 있으며, 모델링에 소요되는 시간이 많이 걸리는 단점이 있다. 따라서 본 논문에서는 새로운 3D 모델링에 대한 접근법의 하나의 단계로서 컬러영상으로부터 경계정보와 색상정보를 활용하여 동일건물을 인식하는 방법에 대하여 연구를 수행하였다. 경계정보에 대해서는 보완된 Hausdorff 거리 개념을 사용하였으며, 색상정보에 대해서는 수정된 컬러 인덱싱 기법을 사용하였다 IKONOS영상을 사용하여 실험을 실시한 결과 두 가지 정보를 각각 단독으로 사용하는 경우 보다는 두 가지 정보를 조합하여 사용하는 경우 인식이 보다 효과적으로 이루어지는 것을 확인할 수 있었다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.25 no.4
• /
• pp.327-336
• /
• 2007

The 3D building modeling is one of crucial components in building 3D geospatial information. The existing methods for 3D building modeling depend mainly on manual photogrammetric processes by stereoplotter compiler, which indeed take great amount of time and efforts. In addition, some automatic methods that were proposed in research papers and experimental trials have limitations of describing the details of buildings with lack of geometric accuracy. It is essential in automatic fashion that the boundary and shape of buildings should be drawn effortlessly by a sophisticated algorithm. In recent years, airborne LiDAR data representing earth surface in 3D has been utilized in many different fields. However, it is still in technical difficulties for clean and correct boundary extraction without human intervention. The usage of airborne LiDAR data will be much feasible to reconstruct the roof tops of buildings whose boundary lines could be taken out from existing digital maps. The paper proposed a method to reconstruct the roof tops of buildings using airborne LiDAR data with building boundary lines from digital map. The primary process is to perform octree-based segmentation to airborne LiDAR data recursively in 3D space till there are no more airborne LiDAR points to be segmented. Once the octree-based segmentation has been completed, each segmented patch is thereafter merged based on geometric spatial characteristics. The experimental results showed that the proposed method were capable of extracting various building roof components such as plane, gable, polyhedric and curved surface.