Automatic description of complex buildings from multiple images

We present an approach for detecting and describing complex buildings with flat or complex rooftops by using multiple, overlapping images of the scene. We find 3-D rooftop boundary hypotheses from the line and junction features of the images by applying consecutive grouping procedures. First, 3-D features are generated by grouping image features over multiple images, and rooftop hypotheses are generated by neighborhood searches on those features. Probabilistic reasoning, level-of-details, and cues from image-derived unedited elevation data are used at various stages to manage the huge search space for rooftop boundary hypotheses. Three-dimensional rooftop hypotheses generated by above procedures are verified with evidence collected from the images and the elevation data. Expandable Bayesian networks are used to combine evidence from multiple images. Finally, overlap and rooftop analyses are performed to find the final building models. Experimental results are shown on complex buildings.