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Home > Archive>Volume 29, Issue 2, 2018 >483-505. DOI:10.13328/j.cnki.jos.005379
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Survey on Feature Extraction Techniques for Non-Rigid 3D Shape Retrieval
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National Natural Science Foundation of China (61320106006, 61532006, 61602015); Beijing Natural Science Foundation (4162019); Beijing Science and Technology Project (Z161100001616004)

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    Abstract:

    Shape descriptor is a concise and informative representation. Feature extraction is a key step in many 3D shape analysis tasks. In recent years, feature extraction technologies of non-rigid 3D shape have attracted a lot of attentions. This paper firstly introduces the evaluation criteria and the datasets which are commonly used as benchmark in non-rigid 3D shape feature extraction. Secondly, based on extensive research on the existing literatures and the latest achievements, the paper categorizes the non-rigid 3D shape descriptors into two types:Hand-Crafted shape descriptors and learning based shape descriptors. The basic ideas, advantage and disadvantage of typical algorithms belong to each category, especially the most recent feature extraction algorithms based on deep learning are analyzed, compared and summarized. Finally, some potential future work is discussed.

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李海生,孙莉,武玉娟,吴晓群,蔡强,杜军平.非刚性三维模型检索特征提取技术研究.软件学报,2018,29(2):483-505

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History
  • Received:May 19,2017
  • Revised:July 16,2017
  • Online: October 09,2017
Links:Institute of Software, CASChina Computer FederationCASNSFC
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