A novel framework for 3D shape retrieval

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• 作者：Ye Jin ; ZhiXun Li ; YingTao Zhang ; XiangLong Tang
• 关键词：Shape retrieval ; Laplace–Beltrami operator ; Spectral geometry ; Shape descriptor
• 刊名：Applied Informatics
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：4
• 期：1
• 全文大小：967KB
• 刊物类别：Computing Methodologies; Bioinformatics; Health Informatics; Computer Imaging, Vision, Pattern Recog
• 刊物主题：Computing Methodologies; Bioinformatics; Health Informatics; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Applications; Statistics for Life Sciences, Medicine, Health Sciences;
• 出版者：Springer Berlin Heidelberg
• ISSN：2196-0089
• 卷排序：4

文摘

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.