Reconstructing 3d Building Wireframes from Multiple Images

Building extraction in urban areas is one of the difficult problems in image understanding and photogrammetry. Building delineations are needed in cartographic analysis, urban area planning, and visualization. Although one pair of images is adequate to find the 3D position of two visibly corresponding image features it is not sufficient to extract the entire building due to hidden features that are not projected into the image pair. This paper presents a new technique to detect and delineate buildings with complex rooftops by extracting roof polygons and matching them using multiple images. The algorithm discussed in this paper starts by segmenting the images into regions. Regions are then classified into roof regions and non-roof regions using a two-layered Neural Network. A rule-based system is then used to convert the roof boundaries to polygons. Polygon correspondence is established geometrically, all possible polygon correspondent sets are considered and the optimal set is selected. Polygon vertices are then refined using the known geometric properties of urban buildings to generate the building wireframes. The algorithm is tested on a number of buildings and the results are evaluated. The RMS error for the extracted building vertices is 0.25m using 1:4000 scale aerial photographs. The results show the completeness and accuracy that this method can provide for extracting complex urban buildings.