Non-rigid 3D Shape Retrieval
Non-rigid 3D Shape Retrieval
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DOI：
10.2312/3dor.20151064
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
Z. Lian;J. Zhang;S. Choi;H. ElNaghy;Jihad El-Sana;T. Furuya;Andrea Giachetti;R. Güler;L. Lai
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文献类型：
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作者：
Z. Lian;J. Zhang;S. Choi;H. ElNaghy;Jihad El-Sana;T. Furuya;Andrea Giachetti;R. Güler;L. Lai
非刚性三维形状检索已成为计算机图形学、计算机视觉、模式识别等领域的研究热点。本文介绍了SHREC'15 Track：Non-rigid 3D Shape Retrieval的研究成果。该跟踪的目的是提供一个公平和有效的平台，以评估和比较目前的非刚性3D形状检索方法的性能，这些方法是由世界各地的不同研究小组开发的。在这个轨道中使用的数据库由1200个3D水密三角形网格组成，这些网格平均分为50个类别。同一类别中的所有模型都是通过实现各种姿势变换从原始3D网格生成的。方法的检索性能使用6种常用的度量（即，PR-图、NN、FT、ST、E-测量和DCG）。总共有37份意见书和11个小组参加了这一轨道。本文的评价结果和对比分析不仅展示了非刚性三维形状检索研究的光明前景，而且指出了该课题的几个有前途的研究方向。
Non-rigid 3D shape retrieval has become a research hotpot in communities of computer graphics, computer vision, pattern recognition, etc. In this paper, we present the results of the SHREC'15 Track: Non-rigid 3D Shape Retrieval. The aim of this track is to provide a fair and effective platform to evaluate and compare the performance of current non-rigid 3D shape retrieval methods developed by different research groups around the world. The database utilized in this track consists of 1200 3D watertight triangle meshes which are equally classified into 50 categories. All models in the same category are generated from an original 3D mesh by implementing various pose transformations. The retrieval performance of a method is evaluated using 6 commonly-used measures (i.e., PR-plot, NN, FT, ST, E-measure and DCG.). Totally, there are 37 submissions and 11 groups taking part in this track. Evaluation results and comparison analyses described in this paper not only show the bright future in researches of non-rigid 3D shape retrieval but also point out several promising research directions in this topic.