1. Field of the Invention
This disclosure relates generally to a computer-based method of rendering an image of a three-dimensional structure from several views of the structure. In particular, the computer-based method for creating a roof model relies upon a statistical method of point pattern matching of an aerial top plan view and one or more aerial perspective views of the roof.
2. Description of the Related Art
The building and insurance industries have historically relied on human beings to evaluate roofs in person, to determine labor and materials needed for repair or replacement. Sending a claims adjuster or a roofing specialist out to each individual property can be time-consuming and costly, especially when many buildings need to be evaluated, for instance, following natural disasters such as hurricanes, floods, hail storms, and the like. If the roof is complex, including multiples of roof features such as hips, gables, dormers, and the like, it may not be feasible for a human being to climb onto the roof to take actual measurements. Consequently, insurance evaluations for such roofs often rely on estimates made by a person standing on the ground, who can only give a rough estimate of the sizes of the roof features.
Recently, imaging and mapping technologies have made possible computer-based calculations of roof dimensions from aerial photographs. A top plan view (“orthogonal”) looking straight down from above, together with one or more different perspective views (“oblique”) looking at an angle from, for example, the north, south, east, or west directions, can be sufficient to generate a three-dimensional (3D) reconstruction depth model of the roof structure. Such a 3D model can include roof features such as dormers, gables, hips, and the like that can add a significant degree of complexity to a roof. Accurate measurements of roof lines and areas can then be made from the 3D model. Such methods pertaining to roofs are described in U.S. Pat. Nos. 8,088,436 and 8,170,840. Furthermore, there are many techniques known in the art (e.g., in the field of computer vision) for generation of 3D models of structures from multiple perspective images. Such 3D architectural images have many applications in the building industry.
In the generation of a 3D roof model, combining information from orthogonal and oblique views of the roof entails an initial step of point matching. First, a set of points is identified on each view to represent the shape of the roof, and then corresponding points from each view are matched. Usually the points are at locations where the roof lines merge. Human beings can easily recognize and associate points from the orthogonal view that match points on the oblique view. For example, it is easy for a human being to identify which points are the highest points on either view of the roof, and which points are the lowest. However, requiring human intervention to perform the step of point matching precludes achieving a fully computer-generated model. When many roof models need to be processed, it is inefficient and cumbersome to interrupt a computerized process to obtain a human-generated data set, and then resume the computerized process.
Unfortunately, existing computerized point matching algorithms for performing such a task (e.g., geometrical contour matching, or the scale invariant feature transform (SIFT) technique of feature matching) tend to be complex and exhaustive. For example, if N=20 points are identified on an orthogonal view of a roof and M=10 points are identified on an oblique view of the roof, if all possible permutations are considered, nearly 200,000 potential point match sets must be evaluated to complete the step of point matching. Thus, a more efficient method of computer-based point matching is desirable.