A novel framework for 3D shape retrieval View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Ye Jin, ZhiXun Li, YingTao Zhang, XiangLong Tang

ABSTRACT

The ability to accurately and effectively search for 3D shape is crucial for many applications. In this study, we proposed a novel framework for 3D shape retrieval. We compensate the loss of high frequencies of heat kernel signature from two aspects. One is to introduce the weight for each point to highlight the details of the salient points. The other is to directly capture microgeometry structure through wave kernel’s access to high frequencies. Thus, our method can capture geometric features at different frequencies of a shape, which satisfy the property of an ideal descriptor. We conduct shape retrieval experiments on a standard benchmark and compared with another heat kernel-based method. Experimental results demonstrate that the proposed method is effective and accurate. More... »

PAGES

3

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40535-016-0030-1

DOI

http://dx.doi.org/10.1186/s40535-016-0030-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029032106


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