Recovery of parametric models from range images: the case forsuperquadrics with global deformations
Solina, F.
Bajcsy, R.
Dept. of Comput. & Inf. Sci., Pennsylvania Univ., Philadelphia, PA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Feb 1990
Volume: 12,
Issue: 2
On page(s): 131-147
ISSN: 0162-8828
References Cited: 38
CODEN: ITPIDJ
INSPEC Accession Number: 3629826
Digital Object Identifier: 10.1109/34.44401
Current Version Published: 2002-08-06
Abstract
A method for recovery of compact volumetric models for shape
representation of single-part objects in computer vision is introduced.
The models are superquadrics with parametric deformations (bending,
tapering, and cavity deformation). The input for the model recovery is
three-dimensional range points. Model recovery is formulated as a
least-squares minimization of a cost function for all range points
belonging to a single part. During an iterative gradient descent
minimization process, all model parameters are adjusted simultaneously,
recovery position, orientation, size, and shape of the model, such that
most of the given range points lie close to the model's surface. A
specific solution among several acceptable solutions, where are all
minima in the parameter space, can be reached by constraining the search
to a part of the parameter space. The many shallow local minima in the
parameter space are avoided as a solution by using a stochastic
technique during minimization. Results using real range data show that
the recovered models are stable and that the recovery procedure is fast
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