## 1 Introduction  
With the rapid development of computer science, 3D models have been widely used for applications such as 3D movies, 3D graphics, CAD, 3D architectural design, etc. Due to the explosive growth of the number of 3D models in recent years, how to accurately find the desired 3D model among the massive number of 3D models and improve the 3D model's reuse rate has become an urgent problem to be solved [1]. There have been some developments in 3D model retrieval [2]. Deep learning has been introduced to 3D reconstruction [3], such as the method based on generative adversarial networks [4]. Meanwhile, deep learning also has a lot of development space in 3D data analysis and understanding [5]. In general, the purpose of content-based 3D model retrieval is to find a 3D model which is similar to the input at the content level. Usually, content-based 3D model retrieval can be divided into the following steps: (1) Input the model to be retrieved; (2) Extract the feature descriptor of the model to be retrieved; (3) Define a suitable retrieval method to automatically calculate the similarity distance between the models; (4) Output search results according to similarity distance ranking [6]. According to different types of 3D model data, the existing content-based 3D model retrieval algorithms [7; 8; 9] can be roughly divided into two categories: (1) Model-based 3D model retrieval methods; (2) View-based 3D model retrieval methods. In model-based 3D model retrieval methods, model geometry information [10], surface area distribution [11], volume information [12], surface area geometry information [13], and so on are usually used to describe the 3D model. For most existing model-based 3D model retrieval methods, it is difficult to obtain model information of objects in practical applications. If the model does not exist, this type of method may need to generate the desired three-dimensionalmodel through the image of the model. The process of 3D modeling takes a lot of time. Meanwhile, the choice of image will also affect the accuracy of the algorithm. It extremely limits the application of this type of method. In the view-based 3D model retrieval method, each object is represented by multiple images from different angles. Figure 1 shows three different models' multiple views. These images can be obtained by a set of cameras or a sequence of virtual cameras. Therefore, the view-based 3D model retrieval can be transformed into a group matching problem between image sets and image sets. View-based 3D model retrieval has become a new research hotspot [14; 15; 16]. Compared with model-based 3D model retrieval methods [17; 18; 19], this type of method has the following advantages: (1) The view-based 3D model retrieval method is more flexible because it does not require virtual 3D model information; (2) The retrieval accuracy of rigid models and partial matching is relatively high; (3) The existing image processing technology can be used to improve the retrieval accuracy; (4) The input requirements are reduced, which is conducive to the use of sketches and 2D images as input for retrieval. Therefore, view-based 3D model retrieval is widely used. Deep learning is a new field of machine learning. Deep learning has been widely used in 3D model retrieval and has achieved excellent performance. Moreover, 3D model retrieval methods based on deep learning can be divided into three research directions depending on their input modes, namely, voxel-based methods [20; 21; 22; 23; 24; 25], point-set-based methods [26; 27; 28; 29; 30], and view-based methods. An object is represented as a 3D mesh in the voxel-based methods and is analyzed by a 3D network. In the point-set-based methods, an object is represented as a set of unordered points, and the point cloud is used for prediction. These two methods can also be collectively referred to as model-based methods. The model-based methods use a 3D convolution filter to convolute a 3D shape in 3D space, thus generating a 3D representation directly from the 3D data [31; 32].