非刚性三维模型检索特征提取技术研究
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摘要
三维模型特征描述符是一种简洁且信息量丰富的表示方式,特征提取是许多三维模型分析处理任务的关键步骤.近年来,针对非刚性三维模型特征提取技术的研究引起了人们的广泛关注.首先,汇总了常用的非刚性三维模型基准数据集和算法评价标准;然后,在广泛调研大量文献和最新成果的基础上,将非刚性三维模型特征分为人工设计的特征描述符和基于学习的特征描述符两大类,并分别加以介绍,对每类方法所包含的典型算法,尤其是近几年基于深度学习的特征提取算法的基本思想、优缺点进行了分析、对比和总结;最后进行总结,并对未来可能的发展趋势进行了展望.
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.
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.
引文
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