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View-based 3D model retrieval via supervised multi-view feature learning

机译：通过有监督的多视图特征学习进行基于视图的3D模型检索

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摘要

With the development of the processing technologies of 3D model and the increasing of 3D model in different application flieds, 3D model retrieval is attracting more and more people's attention. In order to handle this problem, most of approaches focus on the feature extraction form different virtual view. It is hard to guarantee the robustness and also ignore the correlation between both views. Thus, we propose an effective view-based 3D model retrieval method via supervised multi-view feature learning (SMFL). First, the subspace dimension of viusal feature is generated through Singular Value Decomposition (SVD) algorithm. This step is used to select main information from multi-view in order to reduce the final amount of calculation; Secondly, we consider the relationship of multi-view from same class and the correlation between two different classes to make the feature mapping in order to reduce the different of views from the same class and increase the different of views from the difference class; Finally, the projection mapping corresponding to the inner product of each 3D model helps to calculate the similarities between two different 3D models. The extensive experiments are conducted on popular ETH, NTU, MV-RED and PSB 3D model datasets with Zernike moments. The comparative results or The experimental results with existing 3D model retrieval methods show the superiority of the proposed method.
机译：随着3D模型处理技术的发展和3D模型在不同应用中的兴起，3D模型检索越来越受到人们的关注。为了解决这个问题，大多数方法集中于从不同的虚拟视图提取特征。很难保证鲁棒性，也很难忽略两个视图之间的相关性。因此，我们通过监督多视图特征学习（SMFL）提出了一种有效的基于视图的3D模型检索方法。首先，通过奇异值分解（SVD）算法生成视觉特征的子空间维。此步骤用于从多视图中选择主要信息，以减少最终的计算量。其次，考虑同一类的多视图之间的关系以及两个不同类之间的相关性，进行特征映射，以减少同一类的视图之间的差异，并增加不同类的视图之间的差异。最后，与每个3D模型的内部乘积相对应的投影映射有助于计算两个不同3D模型之间的相似度。在具有Zernike矩的流行ETH，NTU，MV-RED和PSB 3D模型数据集上进行了广泛的实验。比较结果或与现有3D模型检索方法的实验结果表明了该方法的优越性。

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