Extended Large Scale Sketch-Based 3D Shape Retrieval
Extended Large Scale Sketch-Based 3D Shape Retrieval
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DOI：
10.2312/3dor.20141058
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发表时间：
2014-04
期刊：
影响因子：
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通讯作者：
Bo Li;Yijuan Lu;Chunyuan Li;A. Godil;Tobias Schreck;Masaki Aono;Martin Burtscher;Hongbo Fu
中科院分区：
文献类型：
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作者：
Bo Li;Yijuan Lu;Chunyuan Li;A. Godil;Tobias Schreck;Masaki Aono;Martin Burtscher;Hongbo Fu
基于草图的大规模三维形状检索在基于内容的三维对象检索领域受到越来越多的关注。本研究的目标是评估不同的基于草图的3D模型检索算法在综合3D模型数据集上使用大规模手绘草图查询数据集的性能。该基准包含12,680张草图和8,987个3D模型，分为171个不同的类别。在这条赛道上，4个小组提交了12次跑动，并使用7种常用的检索性能指标对其检索性能进行了评估。我们希望这一基准、对比评估结果和相应的评估代码能够为3D模型检索界进一步推动这一研究方向的进步。
Large scale sketch-based 3D shape retrieval has received more and more attentions in the community of content-based 3D object retrieval. The objective of this track is to evaluate the performance of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset on a comprehensive 3D model dataset. The benchmark contains 12,680 sketches and 8,987 3D models, divided into 171 distinct classes. In this track, 12 runs were submitted by 4 groups and their retrieval performance was evaluated using 7 commonly used retrieval performance metrics. We hope that this benchmark, the comparative evaluation results and the corresponding evaluation code will further promote the progress of this research direction for the 3D model retrieval community.