Incremental reconstruction of 3D scenes from multiple, complex images
Artificial Intelligence
Detecting buildings in aerial images
Computer Vision, Graphics, and Image Processing
Building detection and description from a single intensity image
Computer Vision and Image Understanding
The ascender system: automated site modeling from multiple aerial images
Computer Vision and Image Understanding
Extracting buildings from aerial images using hierachical aggregation in 2D and 3D
Computer Vision and Image Understanding
Detection and Modeling of Buildings from Multiple Aerial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expandable Bayesian networks for 3D object description from multiple views and multiple mode inputs
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.