Publication Type

Journal Article

Version

acceptedVersion

Publication Date

11-2013

Abstract

In this paper, we propose a novel shape descriptor that is robust in differentiating intrinsic symmetric points on geometric surfaces. Our motivation is that even the state-of-theart shape descriptors and non-rigid surface matching algorithms suffer from symmetry flips. They cannot differentiate surface points that are symmetric or near symmetric. Hence a left hand of one human model may be matched to a right hand of another. Our Symmetry Robust Descriptor (SRD) is based on a signed angle field, which can be calculated from the gradient fields of the harmonic fields of two point pairs. Experiments show that the proposed shape descriptor SRD results in much less symmetry flips compared to alternative methods. We further incorporate SRD into a stand-alone algorithm to minimize symmetry flips in finding sparse shape correspondences. SRD can also be used to augment other modern non-rigid shape matching algorithms with ease to alleviate symmetry confusions.

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Information Systems and Management

Publication

Computer Graphics Forum

Volume

32

Issue

7

First Page

355

Last Page

362

ISSN

0167-7055

Identifier

10.1111/cgf.12243

Publisher

Wiley

Additional URL

https://doi.org/10.1111/cgf.12243

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