Interest points on the building facade are the basic element for 3-D building modeling and texturing. Grouping these points to the same or separate buildings is a fundamental process for establishing building models and detecting building boundaries. The grouping process is generally achieved by analyzing the geometric relation and the distances between the points in the object space, which requires precise interior and exterior orientation camera parameters. In this paper, we propose a method for grouping points on buildings in the image space and focus on the close-range stereo image problems. The highlight of this method is the capability to work with un-calibrated cameras and even with images gathered from the internet, such as Google Street View TM . Every plane in an image pair, such as building facade, has a unique homography matrix, which we exploit as the basis of grouping points lying on this plane. Three major steps in this method: Fundamental matrix estimation, grouping points to planes, and assigning planes to building facades. Projective geometry between the two images can be represented by the fundamental matrix, which can be solved using point correspondences. We apply 2D Delaunay triangulation to define a set of patches in image space. Each patch that reside on the building facade has a corresponding homography matrix can be estimated from the fundamental matrix. We group the patches based on the similarity of the estimated homography matrices for the patches and forming points into planes. Buildings are usually connected and obscured in the photograph taken in a building rich circumstance. Grouping planes into building facade require two additional steps: 1) Separating buildings that are connected in the image but not in the real world, and 2) connecting two or more planes that belong to the same building facade. These resulting building facade can then be transform to 3-D using the geometric constraint or GCPs.
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