Automatic detection and description of cultural features, such as buildings, from aerial images is becoming increasingly important for a number of applications. This task also offers an excellent domain for studying the general problems of scene segmentation, 3D inference, and shape description under highly challenging conditions. We describe a system that detects and constructs 3D models for rectilinear buildings with either flat or symmetric gable roofs from multiple aerial images; the multiple images, however, need not be stereo pairs (i.e., they may be acquired at different times). Hypotheses for rectangular roof components are generated by grouping lines in the images hierarchically; the hypotheses are verified by searching for presence of predicted walls and shadows. The hypothesis generation process combines the tasks of hierarchical grouping with matching at successive stages. Overlap and containment relations between 3D structures are analyzed to resolve conflicts. This system has been tested on a large number of real examples with good results, some of which are included in the paper along with their evaluations.

An approach that allows a user to assist an automatic system in modeling buildings is described. The approach is designed to be efficient in user time and effort while preserving the quality of the models created. Currently our system is able to handle the rectangular buildings with flat roof or symmetric gabled roof. Models can be created by only one or two clicks in many cases. Efficient editing of automatically derived models is also possible.

A method for detection and description of rectangular buildings from two or more registered aerial intensity images is proposed. The output is a 3D description of the buildings, with an associated confidence measure for each building. Hierarchical perceptual grouping and matching across views is employed to increase the robustness of the system. Verification of selected building hypotheses is done using shadow and wall evidence of the buildings. The system is largely feature-based. Grouping and matching are performed in a hierarchical manner utilizing primitives of increasing complexity, starting with line segments and junctions, and proceeding to higher level features. Binocular and trinocular epipolar constraints are used to reduce the search space for matching features.