A Probabilistic Framework for Partial Intrinsic Symmetries in Geometric Data
Abstract
In this paper, we present a novel algorithm for partial
intrinsic symmetry detection in 3D geometry. Unlike previous
work, our algorithm is based on a conceptually simple
and straightforward probabilistic formulation of partial
shape matching: based on a Markov random field model,
we obtain a probability distribution over all possible intrinsic
matches of a shape to itself, which reveals the symmetry
structure of the object. Rather than examining this exponentially
sized distribution directly, which is infeasible,
we approximate marginals of this distribution using sumproduct
loopy belief propagation and show how the symmetry
information can subsequently be extracted from this
condensed representation. Using a parallel implementation
on graphics hardware, we are able to extract symmetries
of deformable shapes in general poses efficiently. We apply
our algorithm on several standard 3D models, demonstrating
that a concise probabilistic model yields a practical and
general symmetry detection algorithm.
Keywords:
belief propagation, symmetry, registration, gpu
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Bibliography
R. Lasowski, A. Tevs, H.-P. Seidel, M. Wand "A Probabilistic Framework for Partial Intrinsic Symmetries in Geometric Data" , IEEE International Conference on Computer Vision (ICCV'09), 2009, to appear. [bibtex]
@inproceedings{LasowskiICCV2009,
author = {Lasowski, Ruxandra and Tevs, Art and Seidel, Hans-Peter and Wand, Michael},
title = {A Probabilistic Framework for Partial Intrinsic Symmetries in Geometric Data},
booktitle = {IEEE International Conference on Computer Vision (ICCV'09)},
publisher = {IEEE Computer Society},
year = {2009},
pages = {toappear},
address = {Koyoto, Japan},
}