Joint Feature Mapping for End-to-End Sketch-Based 3D Model Retrieval

被引：0
作者：
Bai J. [1 ,2 ]
Kong D. [1 ]
Zhou W. [1 ]
Wang M. [1 ]
机构：
[1] School of Computer Science and Engineering, North Minzu University, Yinchuan
[2] Ningxia Province Key Laboratory of Intelligent Information and Data Processing, Yinchuan
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 12期
关键词：
3D model retrieval; Deep learning; End-to-end network; Joint feature distribution; Metric learning; Sketch-based retrieval;
D O I：
10.3724/SP.J.1089.2019.17904
中图分类号：
学科分类号：
摘要：
Sketch based 3D model retrieval has the characteristics including diversity of intra-class sketches, complexity of the 3D models, and the huge inter-domain differences between the sketches and 3D models. The interaction of these characteristics makes the sketch-based 3D model retrieval task becomes particularly difficult. To solve the problem, an end-to-end sketch-3D model retrieval framework based on joint feature mapping is proposed. Firstly, the 3D model is transformed into a set of 2D views to establish the shared data space of cross-domain data. Then, through network weight sharing, the end-to-end triplet metric learning network is established, and the joint feature mapping of the cross-domain data, sketches and views, is realized. Finally, based on the joint feature distribution, four kinds of similarity evaluation algorithms between sketches and 3D models are proposed to realize sketch based 3D model retrieval. The retrieval precisions in the large public data sets SHREC2013 and SHREC2014 are 81.8% and 75.6%, respectively, and have demonstrated that the algorithm in this paper is better than the state-of-the art methods in seven indexes of PR curve, NN, FT, ST, E, DCG and MAP. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
引用
收藏
页码：2056 / 2065
页数：9
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