Superquadric-based object modeling by an iterative segmentation-and-recovery algorithm

In the paper, we propose a systematic approach to object modeling by combining superquadric-fitting and segmentation into an interactive algorithm. It is assumed that the input data are a discrete description of the whole close-surface (CS) of the object, which can be acquired by range image registration and integration. Using the data as input, the method is a top-down, recursive procedure as follows: At first, it finds an initial approximation of the object by fitting a single superquadric to the whole CS data. The residual errors are examined to pick up data points locating in concave regions and far away from the fitted superquadric. A dividing plane is then extracted from the selected points to partition the original data set into two disjoint subsets, which are, respectively, approximated further bg the same fitting-and-splitting process. This process is repeated until the whole data are decomposed into a number of primitive superquadrics each with a satisfactory accuracy. We present results of experiments using real range data for some complex objects.