Modelbase Partitioning Using Property Matrix Spectra

Abstract

In this paper, we present aneigenvalueorspectral-basedrepresentation 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 we presented in a previous paper [IEEE Trans. Pattern Anal. Machine Intell.17, 1995, 321--332]. In recognition, gross features computed from an 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. We also model the perturbations in the eigenvalues computed from objects in real scenes and show how this perturbation model can be used effectively during recognition. The partitioning experiments presented here are for real range images using a modelbase of 125 CAD objects with planar, cylindrical, and spherical surfaces. From our recognition results, we observe that for a reasonable degree of error in the low-level processes (surface segmentation and grouping), the correct partition is always included. Experimental results also point to a significant increase in recognition speed, even on modelbases much smaller than the ones we consider here.