hide
Free keywords:
-
Abstract:
We address the problem of partial symmetry detection, i.e., the identification
of building blocks a complex shape
is composed of. Previous techniques identify parts that relate to each other by
simple rigid mappings, similarity
transforms, or, more recently, intrinsic isometries. Our approach generalizes
the notion of partial symmetries to
more general deformations. We introduce subspace symmetries whereby we
characterize similarity by requiring
the set of symmetric parts to form a low dimensional shape space. We present an
algorithm to discover subspace
symmetries based on detecting linearly correlated correspondences among graphs
of invariant features. The detected
subspace symmetries along with the modeled variations are useful for a variety
of applications including
shape completion, non-local and non-rigid denoising. We evaluate our technique
on various data sets. We show
that for models with pronounced surface features, subspace symmetries can be
found fully automatically. For
complicated cases, a small amount of user input is used to resolve ambiguities.
Our technique computes dense
correspondences that can subsequently be used in various applications, such as
model repair and denoising.