# FAmesh: Generating Frequency Adaptive Meshes from Single Images under 2D Hole Constraints
## 4 Result and Discussion
### Ablation Study  
We divide the 13 classes of objects into those with holes (chairs, benches, and tables), those with rich details (lamps, firearms, and watercraft), and the other classes. Table 2 displays the results of the ablation experiment. Removing the pruning component had the greatest effect on the target with holes, increasing the CD value by 0.45 and decreasing the F-score by 6.22. This shows that pruning components can improve complex structures. When the local refinement part is deleted, it has the greatest impact on objects with rich details; the CD value increases by 0.459, and the F-score decreases by 4.3. This shows that the part improves more on objects with rich detail. Figure 7 is the corresponding visualization result. The result of the full model can have the correct holes, and the visual effect is best on the high and low curvature parts. The reason for the above experimental results is that the prune component can improve the network performance by pruning the holes of the object, so the improvement of the network is mainly concentrated on the objects with holes, and the local refinement component adaptively handles the features of different frequencies. The improvement is evident for both high and low curvature parts (especially high curvature parts). We further analyzed the time consumption of the designed components. Table 3 shows that the time required for pruning is 32.1 ms, and the time required for refining is 34.0 ms. The time required for the full model to perform an inference is 51.9 ms, and the inference speed of the model is acceptable. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**Category**} & \multicolumn{6}{c}{**CD**} & \multicolumn{6}{c}{**F-Score**} \\ \cline{2-13}  & **Pixel2 mesh** & **Atlasnet** & **TMNET** & **Vanet** & **Ours** & \(\Delta\) & **Pixel2 mesh** & **Atlasnet** & **TMNET** & **Vanet** & **Ours** & \(\Delta\) \\ \hline plane & 2.547 & 1.539 & 1.325 & 2.317 & **0.912** & \(-\)0.413 & 76.93 & 89.45 & 87.79 & 79.31 & **93.78** & 5.99 \\ bench & 2.833 & 2.112 & 2.042 & 2.874 & **1.768** & \(-\)0.274 & 74.46 & 79.71 & 81.82 & 70.45 & **88.03** & 6.21 \\ cabinet & 2.394 & 2.168 & 1.823 & 2.651 & **1.817** & \(-\)0.006 & 78.17 & 79.47 & 79.10 & 76.74 & **80.53** & 1.43 \\ car & 2.035 & 2.427 & 1.592 & 2.789 & **1.092** & \(-\)0.500 & 82.74 & 83.14 & 88.49 & 83.47 & **90.43** & 1.94 \\ chair & 2.771 & 1.731 & 1.726 & 2.510 & **1.238** & \(-\)0.488 & 75.49 & 82.47 & 83.74 & 78.24 & **84.79** & 1.05 \\ monitor & 3.043 & **2.125** & 2.423 & 3.247 & 2.467 & 0.044 & 70.03 & 77.92 & 78.68 & 72.03 & **78.92** & 0.24 \\ lamp & 5.124 & 4.049 & 5.348 & 4.515 & **3.029** & \(-\)2.319 & 62.36 & 66.22 & 67.48 & 64.54 & **74.21** & 6.73 \\ speaker & 3.574 & 3.026 & 3.276 & 3.724 & **2.929** & \(-\)0.347 & 72.45 & 74.21 & 73.40 & 74.36 & **75.38** & 1.98 \\ firearm & 2.142 & 2.454 & 1.894 & 2.482 & **0.949** & \(-\)0.945 & 83.79 & 83.76 & 86.27 & 83.50 & **93.22** & 6.95 \\ couch & 2.357 & 1.938 & **1.295** & 2.677 & 1.594 & 0.299 & 80.14 & 84.06 & 87.23 & 82.37 & **87.33** & 0.10 \\ table & 5.809 & 2.742 & **2.416** & 5.939 & **1.832** & \(-\)0.584 & 65.48 & 79.47 & 81.63 & 67.64 & **86.65** & 5.02 \\ phone & 1.963 & 1.473 & **1.086** & 1.647 & 1.371 & 0.285 & 85.15 & 88.63 & 90.05 & 86.20 & **90.17** & 0.12 \\ vessel & 3.016 & 2.318 & 2.113 & 3.546 & **1.923** & \(-\)0.190 & 79.31 & 80.21 & 82.94 & 79.41 & **86.77** & 3.83 \\ \hline mean & 3.047 & 2.316 & 2.181 & 3.148 & **1.763** & \(-\)0.418 & 75.88 & 80.67 & 82.20 & 76.79 & **85.40** & 3.20 \\ \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparisons (CD \(\times\) 0.001, F-score \(\times\) 0.01) of our method against state-of-the-arts. For CD, the smaller the better. For F-score, the higher the better. \(\Delta\) represents the difference between our method and TMNET.