Persistent Heat Signature for Pose-oblivious Matching of Incomplete Models

Although understanding of shape features in the context of shape matching and retrieval has made considerable progress in recent years, the case for partial and incomplete models in presence of pose variations still begs a robust and efficient solution. A signature that encodes features at multi‐sca...

Full description

Saved in:
Bibliographic Details
Published in:Computer graphics forum Vol. 29; no. 5; pp. 1545 - 1554
Main Authors: Dey, T.K., Li, K., Luo, C., Ranjan, P., Safa, I., Wang, Y.
Format: Journal Article
Language:English
Published: Oxford, UK Blackwell Publishing Ltd 01.07.2010
Subjects:
Algorithms
and object representations
Computer based modeling
Computer graphics
Geometry
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Curve
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Curve, surface, solid, and object representations
I.3.8 [Computer Graphics]: Applications
Invariants
Kernels
Matching
Mathematical analysis
Mathematical models
Robustness
Signatures
solid
Studies
surface
ISSN:0167-7055, 1467-8659
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Although understanding of shape features in the context of shape matching and retrieval has made considerable progress in recent years, the case for partial and incomplete models in presence of pose variations still begs a robust and efficient solution. A signature that encodes features at multi‐scales in a pose invariant manner is more appropriate for this case. The Heat Kernel Signature function from spectral theory exhibits this multi‐scale property. We show how this concept can be merged with the persistent homology to design a novel efficient pose‐oblivious matching algorithm for all models, be they partial, incomplete, or complete. We make the algorithm scalable so that it can handle large data sets. Several test results show the robustness of our approach.
Bibliography:istex:8BA5912E18AB2DA7A2F3834EF166429240368598
ark:/67375/WNG-TGMQWWS9-K
ArticleID:CGF1763
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2010.01763.x