# MixFormer: A Self-Attentive Convolutional Network for 3D Mesh Object Recognition
## 4 Results and Discussion
### Classification Experiments  
#### 4.2.1 Classification Experiments Based on SHREC15 Dataset  
The SHREC15 dataset is derived from SHREC11 and SHREC14 and contains both rigid and non-rigid models. The SHREC15 dataset contains 50 categories with 24 models in each category, for a total of 1200 3D mesh models. The maximum mesh resolution in the dataset is 60,210 vertices, and the average mesh resolution is 21,141 vertices. In the ratio of 3:1, 18 3D mesh models are randomly selected from each category as the training set and the rest as the test set.  
In order to verify the classification effect of MixFormer proposed in this paper, this paper compares the traditional manual feature-based classification method SPH with the 3D convolution-based classification methods MeshNet and MeshCNN. The accuracy and parametric number comparison of each method for classification in the SHREC15 dataset are given in Table 1.  
Due to the large number of classes included in SHREC15, only the class classification accuracies and the average accuracy of each method for all real columns for 11 of these models are shown in Table 1. It can be seen that the classification effect of the MixFormer proposed in this paper is better than other comparison methods, and the classification accuracy is 96.7%, which is 5 percentage points better than the optimal method MeshCNN among the comparison methods. This shows that the MixFormer proposed in this paper does have a good ability to learn features on 3D grid models and can achieve good classification results without deep network architecture  
Table 2 compares the time and space complexity of the methods in this paper with those of other classification methods. The column Parmas shows the total number of parameters of the network, and the column FLOPs shows the number of floating point operations performed on each input sample, representing the spatial and temporal complexity, respectively. By learning local feature information through the front 3D grid convolution module and establishing dependencies between features through the 3D grid transformer, MixFormer has a stronger feature learning capability, so good learning results can be achieved without stacking multiple layers and with less computational overhead.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**Class Accuracy/\%**} & \multirow{2}{*}{**Accuracy/\%**} \\ \cline{2-2} \cline{4-13}  & **Alien** & & & & & & & & & & & \\ \hline SPH & 87.4 & 86.2 & 90.4 & 89.3 & 86.7 & 89.1 & 90.2 & 89.4 & 87.1 & 89.9 & 88.1 & 88.2 \\ MeshNet & 89.5 & 89.6 & 89.6 & 91.4 & 90.5 & 90.8 & 90.1 & 89.8 & 88.0 & 91.4 & 90.3 & 90.4 \\ MeshCNN & 91.2 & 91.4 & 92.1 & 90.2 & 93.7 & 91.6 & 92.7 & 90.5 & 91.8 & 90.4 & 90.3 & 91.7 \\ Ours & 100 & 100 & 100 & 87.5 & 100 & 100 & 100 & 100 & 100 & 75.0 & 96.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy of different methods on SHREC15.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Parmas/M** & **FLOPs/10\({}^{9}\)** & **Accuracy/\%** \\ \hline SPH & 2.4 & 4.4 & 88.2 \\ MeshNet & 4.3 & 5.1 & 90.4 \\ MeshCNN & 1.3 & 5.0 & 91.7 \\ Ours & 0.8 & 1.2 & 96.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the classification accuracy of different pooling sampling methods.