Using spectral features for modelbase partitioning

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