CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Global and Local Isometry-Invariant Descriptor for 3D Shape Comparison and Partial Matching
Huai-Yu Wu; Hongbin Zha; Tao Luo; Xu-Lei Wang; Songde MA
2010
Conference NameIEEE Conference on Computer Vision and Pattern Recognitionn (CVPR 2010)
Source PublicationIEEE Conference on Computer Vision and Pattern Recognitionn (CVPR 2010)
Conference Date2010
Conference PlaceSan Francisco, California
Abstract
In this paper, based on manifold harmonics, we propose
a novel framework for 3D shape similarity comparison and
partial matching. First, we propose a novel symmetric meanvalue
representation to robustly construct high-quality manifold
harmonic bases on nonuniform-sampling meshes. Then,
based on the manifold harmonic bases constructed, a novel
shape descriptor is presented to capture both of global and local
features of 3D shape. This feature descriptor is isometryinvariant,
i.e., invariant to rigid-body transformations and
non-rigid bending. After characterizing 3D models with the
shape features, we perform 3D retrieval with a up-to-date discriminative
kernel. This kernel is a dimension-free approach
to quantifying the similarity between two unordered featuresets,
thus especially suitable for our high-dimensional feature
data. Experimental results show that our framework can be effectively
used for both comprehensive comparison and partial
matching among non-rigid 3D shapes.
KeywordGlobal And Local Isometry-invariant Descriptor For 3d Shape Comparison And Partial Matching
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12141
Collection模式识别国家重点实验室_先进数据分析与学习
Corresponding AuthorHuai-Yu Wu
AffiliationNLPR, CASIA
Recommended Citation
GB/T 7714
Huai-Yu Wu,Hongbin Zha,Tao Luo,et al. Global and Local Isometry-Invariant Descriptor for 3D Shape Comparison and Partial Matching[C],2010.
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