26 September 1997 Superquadric-based object modeling by an iterative segmentation-and-recovery algorithm
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Abstract
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 by 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.
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Hongbin Zha, Hongbin Zha, Tsuyoshi Hoshide, Tsuyoshi Hoshide, Tsutomu Hasegawa, Tsutomu Hasegawa, } "Superquadric-based object modeling by an iterative segmentation-and-recovery algorithm", Proc. SPIE 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling, (26 September 1997); doi: 10.1117/12.290323; https://doi.org/10.1117/12.290323
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