Using spectral features for modelbase partitioning
K. Sengupta, Kim L. Boyer · 1996
We present an eigenvalue or spectral representation for CAD models to be used in conjunction with the more traditional attributed graph based representation of these models. The eigenvalues provide a gross description of the structure of the objects, and help to divide a large modelbase into structurally homogeneous partitions. Models in each partition are next hierarchically organized according to the algorithm presented in Sengupta and Boyer (1995). In recognition, gross features computed from a hypothesized object in a range image are used to prune the modelbase by selecting a few "favorable" partitions in which the correct object model is likely to lie. The partitioning experiments presented here are for real range images using a modelbase of 125 CAD objects with planar, cylindrical, and spherical surfaces.