## 2 Related Works  
Three-dimensional object recognition is a fundamental task in computer vision, with applications spanning robotics, autonomous driving, and augmented reality. Traditional methods, such as PointNet [8] and PointNet++ [9], process raw 3D point clouds to learn geometric features. While effective, these approaches can be sensitive to data sparsity and occlusions, and their computational complexity may limit scalability. To address these challenges, multi-view methods project 3D objects into 2D images captured from various perspectives. For instance, multi-view-based methods aggregate features across views using CNNs or ViTs, leveraging 2D image processing techniques to achieve notable accuracy [10]. However, the static aggregation of view-level features restricts the capacity to capture the dynamic dependencies that exist across different views. The exciting progress in the recognition of 3D objects based on deep learning with different representations, such as meshes, point clouds, voxels, and multi-view, is as follows. The first presentation of a 3D object is a 3D mesh, which is a structure consisting of various polygons. However, the obvious obstacle is the irregular topology and arbitrary resolution, which is impossible to feed into traditional deep learning models. To tackle this issue, [PERSON] et al. proposed two different convolutional restricted Boltzmann machine-based deep learning methods to learn 3D local and global features [11; 12]. [PERSON] et al. presented a novel MeshNet to solve the complexity and irregularity problem of meshes by introducing face units and a feature-splitting mechanism. Their experimental results indicated the effectiveness of 3D shape representation [13]. In [14], [PERSON] et al. proposed a novel deep learning approach that leverages a self-attention mechanism and edge attention module to learn global features, aggregates features of adjacent edges to learn local features, abandons pooling layers, and adopts a spatial position encoding module. The point cloud is another 3D geometric data format that cannot be processed directly with typical convolutional architecture. PointNet [8] and its enhanced variant PointNet++ [9] were pioneering frameworks designed to learn spatial point features directly from raw point clouds, which significantly advanced research in 3D object detection and recognition. [PERSON] et al. presented a self-organizing network (SONet) that takes the point cloud and a self-organizing map as the input to learn global features [15]. Point2 Sequence [16] employed an RNN-based sequence model for local regions in point clouds to capture the correlation by aggregating multiscale areas with attention. Moreover, X-3D captures explicit local structural information from the input 3D space and leverages it to generate dynamic kernels, where weight sharing is adopted across all neighboring points within the same local region to enhance computational efficiency [17]. Since meshes and point clouds are irregular formats for 3D representation, most researchers typically transform them into regular 3D voxel grids or a set of 2D view images, which are acceptable formats for CNN models. To this end, 3D ShapeNet was proposed to represent a 3D geometric shape as a probabilistic distribution of binary variables on a 3D voxel grid [18]. Moreover, methods have been implemented directly on the 3D objects, which usually represent them as a collection of voxels in 3D Euclidean space [19] and then analyze them with a 3D-DL network to learn the features for recognition [20; 21]. Advancements in temporal modeling have significantly influenced tasks involving sequential views, such as SV2 SL1 [22], 3D2 SV [23], and MVCLN [24]. In the video-based action recognition task, several methods exploit the structure of spatial cross-correlations from consecutive frames to estimate optical flow [25; 26] or to learn motion features for action recognition [27; 28].