With the development of 3D model reconstruction, manufacturing, and 3D model vision technologies, 3D model recognition has attracted much attention recently. To handle the 3D model recognition problem, in this paper, we propose a panorama based on multi-channel-attention (MCA) CNN network for the representation of the 3D model. The proposed method is composed of three parts: extracting views, transform function learning, and generating 3D model descriptor. Concretely, we first extract the 2D panoramic views for each 3D model, and we use the multi-channel-attention neural network to extract the descriptor for each 3D model. Here, the attention model is used to find the unequal weights of each panorama view to generate the more robust 3D model descriptor. Finally, The fusion feature is used to handle the 3D model classification and retrieval problem. The popular data sets ModelNet and ShapeNet are used to demonstrate the performance of our approach. The experiments also demonstrate the superiority of our proposed method over the state-of-art methods.

中文翻译：

---

基于多通道注意力CNN的全景图用于3D模型识别

随着3D模型重建、制造、3D模型视觉技术的发展，3D模型识别近来备受关注。为了处理 3D 模型识别问题，在本文中，我们提出了一种基于多通道注意 (MCA) CNN 网络的全景图来表示 3D 模型。所提出的方法由三部分组成：提取视图、变换函数学习和生成3D模型描述符。具体来说，我们首先提取每个 3D 模型的 2D 全景视图，然后使用多通道注意力神经网络提取每个 3D 模型的描述符。在这里，注意力模型用于找到每个全景视图的不相等权重，以生成更健壮的 3D 模型描述符。最后，融合特征用于处理3D模型分类和检索问题。流行的数据集 ModelNet 和 ShapeNet 用于演示我们方法的性能。实验还证明了我们提出的方法优于最先进的方法。