ZS3D-Net: Zero-Shot Classification Network for 3D Models

被引：0
|
作者：
Bai J. [1 ,2 ]
Yuan T. [1 ]
Fan Y. [1 ]
机构：
[1] School of Computer Science and Engineering, North Minzu University, Yinchuan
[2] Key Laboratory of Images, Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2022年 / 34卷 / 07期
关键词：
3D model classification; deep learning; semantic manifold embedding; zero-shot learning;
D O I：
10.3724/SP.J.1089.2022.19173
中图分类号：
学科分类号：
摘要：
Zero-shot 3D model classification is very important for the understanding and analysis of 3D models. Aiming at the problems of lack of corresponding datasets and low accuracy of zero-shot 3D model classification, a 3D model dataset ZS3D is constructed and a deep learning network ZS3D-Net is proposed. The dataset consists of 41 classes, 1677 non-rigid 3D models with complete attributes of all classes, which can be regarded as the benchmark for zero-shot 3D model classification task. For the network, firstly, the visual features of the 3D models are effectively extracted through an ensemble learning sub-network. Then, the correlation between the visual features and semantic features of the unseen and seen classes can be constructed by a semantic manifold embedding sub-network. Finally, the unseen classes can be recognized based on above two sub-networks. On a traditional 3D model dataset and the proposed ZS3D, ZS3D-Net achieves 30.0% and 58.6% classification accuracy respectively, which are on par or better than the state-of-the-art methods. The experiments also demonstrate that the proposed method has good feasibility and validity. © 2022 Institute of Computing Technology. All rights reserved.
引用
收藏
页码：1118 / 1126
页数：8
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