# Research on an Optimal Path Planning Method Based on A* Algorithm for Multi-View Recognition
## 2 Related Works  
### Multi-View Target Recognition  
The view-based approach is widely used in the 3D object recognition, that is, 3D object recognition problem is transformed into the recognition of multiple 2D images. Features are captured from multiple viewpoints and fused to improve the recognition rate. Due to the dimensionality reduction, 2D images only need 2D convolution filters for feature learning, which greatly reduces the computation. The images acquired from different viewpoints can also complement the detailed features of the target. Therefore, the view-based multi-view target recognition method with excellent adaptability and flexibility has become a common method for three-dimensional object recognition and detection, and has gained the attention of many research scholars. The model, based on the multi-view convolutional neural network (MVCNN) [46], is a milestone in multi-view target recognition. It plays a pioneering role by fusing features from multiple views through a maximum pooling layer to generate a simple and efficient descriptor. [PERSON] et al. [47] unified feature extraction and target recognition into a CNN, and proposed an end-to-end pairwise multi-view convolutional neural network. Slice and Concat layers were added and a multi-view pairwise recognition model [48] was established, which could discriminate multiple batches of inputs to improve the robustness of feature extraction. [PERSON] et al. [49] built an end-to-end multi-view three-dimensional fingerprint learning model based on full convolutional networks and Siamese networks. [PERSON] et al. [50] developed a SeqViews2 SeqLabels model. An attention module was used to assign weights for each view. Sequential views were further aggregated to improve the recognition accuracy. [PERSON] et al. [51] proposed a lightweight multi-view network (PVLNet) based on a parametric view learning mechanism. Views are directly constructed as parameters of PVLNet under the guidance of the parametric view learning mechanism. The gradient descent method was used for automatic optimization. A simplified microscopic deep graph generator was used to ensure the gradient propagation of the images, and the global maximum pooling was used to aggregate the multi-view features extracted by the CNN. The recognition efficiency was significantly improved. In addition, the fusion of view features is one of the research trends used to reduce the number of operations. [PERSON] et al. [52] proposed a classification method based on semi-supervised learning, which included CNN-LP (label propagation) with label prop agation capability and multi-view fusion strategy based on expectation maximization. [PERSON] et al. (2020) designed a multi-view based local information fusion network (MV-LFN) for a 3D shape recognition task. A local relevance attention mechanism was introduced to generate more efficient view descriptors. Local information was effectively extracted and maintained by hierarchical aggregation of multi-view feature maps, which significantly improved the recognition capability. [PERSON] et al. (2020) coded the multi-view feature projections as binary codes, and assembled them into block statistical features as recognition descriptors. [PERSON] et al. (2020) proposed a new multi-view based hierarchical fusion pooling method (MHFP) for 3D model identification, which hierarchically fused features from multiple views into a compact descriptor. A 3D attention module was designed to mine the correlation between views. Redundant information was removed effectively, and a large amount of essential information was retained.