Three-dimensional (3D) shape recognition is a popular topic and has potential application value in the field of computer vision. With the recent proliferation of deep learning, various deep learning models have achieved state-of-the-art performance. Among them, multiview-based 3D shape representation has received increased attention in recent years, and related approaches have shown significant improvement in 3D shape recognition. However, these methods focus on feature learning based on the design of the network and ignore the correlation among views. In this article, we propose a novel progressive feature guide learning network (PGNet) that focuses on the correlation among multiple views and integrates multiple modalities for 3D shape recognition. In particular, we propose two information fusion schemes from visual and feature aspects. The visual fusion scheme focuses on the view level and employs the soft-attention model to define the weights of views for visual information fusion. The feature fusion scheme focuses on the feature dimension information and employs the quantified feature as the mask to further optimize the feature. These two schemes jointly construct a PGNet for 3D shape representation. The classic ModelNet40 and ShapeNetCore55 datasets are applied to demonstrate the performance of our approach. The corresponding experiment also demonstrates the superiority of our approach.

中文翻译：

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PGNet：用于三维形状识别的渐进式特征引导学习网络

三维（3D）形状识别是一个热门话题，在计算机视觉领域具有潜在的应用价值。随着最近深度学习的普及，各种深度学习模型都取得了最先进的性能。其中，基于多视图的 3D 形状表示近年来受到越来越多的关注，相关方法在 3D 形状识别方面表现出显着的进步。然而，这些方法侧重于基于网络设计的特征学习，而忽略了视图之间的相关性。在本文中，我们提出了一种新颖的渐进式特征引导学习网络（PGNet），该网络专注于多个视图之间的相关性，并集成了多种模态以进行 3D 形状识别。特别是，我们从视觉和特征方面提出了两种信息融合方案。视觉融合方案侧​​重于视图级别，并采用软注意模型来定义视图的权重以进行视觉信息融合。特征融合方案关注特征维度信息，以量化后的特征为掩码，进一步优化特征。这两个方案共同构建了一个用于 3D 形状表示的 PGNet。经典的 ModelNet40 和 ShapeNetCore55 数据集用于展示我们方法的性能。相应的实验也证明了我们方法的优越性。这两个方案共同构建了一个用于 3D 形状表示的 PGNet。经典的 ModelNet40 和 ShapeNetCore55 数据集用于展示我们方法的性能。相应的实验也证明了我们方法的优越性。这两个方案共同构建了一个用于 3D 形状表示的 PGNet。经典的 ModelNet40 和 ShapeNetCore55 数据集用于展示我们方法的性能。相应的实验也证明了我们方法的优越性。