834 research outputs found
Semi-automatic 3D reconstruction of urban areas using epipolar geometry and template matching
WOS:000240143800002 (Nº de Acesso Web of Science)In this work we describe a novel technique for semi-automatic three-dimensional (3D) reconstruction of urban areas, from airborne stereo-pair images whose output is VRML or DXF. The main challenge is to compute the relevant information—building's height and volume, roof's description, and texture—algorithmically, because it is very time consuming and thus expensive to produce it manually for large urban areas. The algorithm requires some initial calibration input and is able to compute the above-mentioned building characteristics from the stereo pair and the availability of the 2D CAD and the digital elevation model of the same area, with no knowledge of the camera pose or its intrinsic parameters. To achieve this, we have used epipolar geometry, homography computation, automatic feature extraction and we have solved the feature correspondence problem in the stereo pair, by using template matching
A Framework for SAR-Optical Stereogrammetry over Urban Areas
Currently, numerous remote sensing satellites provide a huge volume of
diverse earth observation data. As these data show different features regarding
resolution, accuracy, coverage, and spectral imaging ability, fusion techniques
are required to integrate the different properties of each sensor and produce
useful information. For example, synthetic aperture radar (SAR) data can be
fused with optical imagery to produce 3D information using stereogrammetric
methods. The main focus of this study is to investigate the possibility of
applying a stereogrammetry pipeline to very-high-resolution (VHR) SAR-optical
image pairs. For this purpose, the applicability of semi-global matching is
investigated in this unconventional multi-sensor setting. To support the image
matching by reducing the search space and accelerating the identification of
correct, reliable matches, the possibility of establishing an epipolarity
constraint for VHR SAR-optical image pairs is investigated as well. In
addition, it is shown that the absolute geolocation accuracy of VHR optical
imagery with respect to VHR SAR imagery such as provided by TerraSAR-X can be
improved by a multi-sensor block adjustment formulation based on rational
polynomial coefficients. Finally, the feasibility of generating point clouds
with a median accuracy of about 2m is demonstrated and confirms the potential
of 3D reconstruction from SAR-optical image pairs over urban areas.Comment: This is the pre-acceptance version, to read the final version, please
go to ISPRS Journal of Photogrammetry and Remote Sensing on ScienceDirec
Detecting shadows and low-lying objects in indoor and outdoor scenes using homographies
Many computer vision applications apply background suppression techniques for the detection and segmentation of moving objects in a scene. While these algorithms tend to work well in controlled conditions they often fail when applied to unconstrained real-world environments. This paper describes a system that detects and removes erroneously segmented foreground regions that are close to a ground plane. These regions include shadows, changing background objects and other low-lying objects such as leaves and rubbish. The system uses a set-up of two or more cameras and requires no 3D reconstruction or depth analysis of the regions. Therefore, a strong camera calibration of the set-up is not necessary. A geometric constraint called a homography is exploited to determine if foreground points are on or above the ground plane. The system takes advantage of the fact that regions in images off the homography plane will not correspond after a homography transformation. Experimental results using real world scenes from a pedestrian tracking application illustrate the effectiveness of the proposed approach
Depth Recovery with Rectification using Single-Lens Prism based Stereovision System
Ph.DDOCTOR OF PHILOSOPH
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