Abstract: A computer model of a physical structure (or object) can be generated using context-based hypothesis testing. For a set of point data, a user selects a context specifying a geometric category corresponding to the structure shape. The user specifies at least one seed point from the set that lies on a surface of the structure of interest. Using the context and point data, the system loads points in a region near the seed point(s), and determines the dimensions and orientation of an initial surface component in the context that corresponds to those points. If the selected component is supported by the points, that component can be added to a computer model of the surface. The system can repeatedly find points near a possible extension of the surface model, using the context and current surface component(s) to generate hypotheses for extending the surface model to these points.

Abstract: Large data sets can be stored and processed in real time by combining and registering the large data sets into a single data set. The data can be stored in a data tree structure formed of layers of spatially organized blocks of data. Such storage allows portions of the data to be viewed efficiently, displaying actual point data at an acceptable resolution for the viewing mechanism. Density limited queries can be executed that allow sub-sampling to be done directly and evenly without geometric constraint, to provide a subset of points that is limited in size and includes a spatially-even decomposition of that set of points. This allows the system as a whole to support arbitrarily large point sets while allowing full partitioning functionality, which is efficient to use in both time and space.