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Automated Architectural Reconstruction Using Reference Planes under Convex Optimization
My-Ha Le,찐황헌,Van-Dung Hoang,조강현 제어·로봇·시스템학회 2016 International Journal of Control, Automation, and Vol.14 No.3
In this paper, a method for the automated reconstruction of architectures from two views of a monocularcamera is proposed. While this research topic has been studied over the last few decades, we contend thata satisfactory approach has not yet been devised. Here, a new method to solve the same problem with severalpoints of novelty is proposed. First, reference planes are automatically detected using color, straight lines, andedge/vanishing points. This approach is quite robust and fast even when different views and complicated conditionsare presented. Second, the camera pose and 3D points are accurately estimated by a two-view geometry constraintin the convex optimization approach. It has been demonstrated that camera rotations are appropriately estimated,while translations induce a significant error in short baseline images. To overcome this problem, we rely only onreference planes to estimate image homography instead of using the conventional camera pose estimation method. Thus, the problem associated with short baseline images is adequately addressed. The 3D points and translationare then simultaneously triangulated. Furthermore, both the homography and 3D point triangulation are computedvia the convex optimization approach. The error of back-projection and measured points is minimized in L∞-normso as to overcome the local minima problem of the canonical L2-norm method. Consequently, extremely accuratehomography and point clouds can be achieved with this scheme. In addition, a robust plane fitting method is introducedto describe a scene. The corners are considered as properties of the plane in order to limit the boundary. Thus,it is necessary to find the exact corresponding corner positions by searching along the epipolar line in the secondview. Finally, the texture of faces is mapped from 2D images to a 3D plane. The simulation results demonstrate theeffectiveness of the proposed method for scenic images in an outdoor environment.
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자율주행 로봇의 외부환경 이해를 위한 기하학적인 빌딩 분석
김대년(Dae-Nyeon Kim),찐황헌(Hoang-Hon Trinh),조강현(Kang-Hyun Jo) 제어로봇시스템학회 2010 제어·로봇·시스템학회 논문지 Vol.16 No.3
This paper describes an approach to analyze geometrical information of building images for understanding outdoor environment of autonomous navigation robot. Line segments and color information are used to classify a building with the other objects such as sky, trees, and roads. The line segments and their two neighboring regions are extracted from detected edges in image. The model of line segment (MLS) consists of color information of neighbor regions. This model rules out the line segments of non-building face. A building face converges into dominant vanishing points (DVPs) which include one vertical point and one of five horizontal points in maximum. The intersection of vertical and horizontal lines creates a facet of building. The geometrical characteristics such as the center coordinates, area, aspect ratio and aligned coexistence are used for extracting the windows in the building facet. In experiments, 150 building faces and 1607 windows were detected from the database of outdoor environment. We found that this result shows 94.46% detection rate. These experimental images were all taken in Ulsan metropolitan city in Korea under difference of viewpoints, daytime, camera system and weather condition.
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