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
会议名称IEEE Conference on Computer Vision and Pattern Recognitionn (CVPR 2010)
会议录名称IEEE Conference on Computer Vision and Pattern Recognitionn (CVPR 2010)
会议日期2010
会议地点San Francisco, California
摘要
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.
关键词Global And Local Isometry-invariant Descriptor For 3d Shape Comparison And Partial Matching
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12141
专题模式识别国家重点实验室_先进数据分析与学习
通讯作者Huai-Yu Wu
作者单位NLPR, CASIA
推荐引用方式
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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