# 3D Reconstruction Based on Iterative Optimization of Moving Least-Squares Function
## 4 Experiments
### Result
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Class Name** & **DeepSDF** & **MeshP** & **LIG** & **IMNET** & **NP** & **DeepMLS** & **Present** \\ \hline Display & 0.932 & 0.974 & 0.926 & 0.574 & 0.964 & 0.973 & **0.988** \\ Lamp & 0.864 & 0.963 & 0.882 & 0.592 & 0.930 & 0.922 & **0.963** \\ Airplane & 0.872 & 0.955 & 0.817 & 0.550 & 0.947 & 0.937 & **0.964** \\ Cabinet & 0.872 & 0.957 & 0.948 & 0.700 & 0.930 & 0.955 & **0.986** \\ Vessel & 0.841 & 0.953 & 0.847 & 0.574 & 0.941 & 0.932 & **0.964** \\ Table & 0.901 & 0.962 & 0.936 & 0.702 & 0.908 & 0.962 & **0.977** \\ Chair & 0.886 & 0.962 & 0.920 & 0.820 & 0.937 & 0.950 & **0.955** \\ Sofa & 0.906 & 0.971 & 0.944 & 0.818 & 0.951 & 0.963 & **0.982** \\ Mean & 0.884 & 0.962 & 0.903 & 0.666 & 0.939 & 0.949 & **0.972** \\ \hline \end{tabular}
\end{table} 
Table 1: Results of NC (\(\uparrow\)) of each method in the ShapeNet dataset. (The bold font indicates that it is the optimal result). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Class Name** & **DeepSDF** & **MeshP** & **LIG** & **IMNET** & **NP** & **DeepMLS** & **Present** \\ \hline Display & 0.632 & 0. 903 & 0.551 & 0.601 & 0.989 & 0.994 & **0.998** \\ Lamp & 0.268 & 0.855 & 0.624 & 0.836 & 0.891 & 0.979 & **0.990** \\ Airplane & 0.350 & 0.844 & 0.564 & 0.698 & 0.996 & 0.992 & **0.998** \\ Cabinet & 0.573 & 0.860 & 0.733 & 0.343 & 0.980 & 0.981 & **0.987** \\ Vessel & 0.323 & 0.862 & 0.467 & 0.147 & 0.985 & 0.987 & **0.996** \\ Table & 0.577 & 0.880 & 0.844 & 0.425 & 0.922 & **0.987** & 0.975 \\ Chair & 0.447 & 0.875 & 0.710 & 0.181 & 0.954 & 0.982 & **0.985** \\ Sofa & 0.577 & 0.895 & 0.822 & 0.199 & 0.968 & 0.987 & **0.994** \\ Mean & 0.468 & 0.872 & 0.664 & 0.429 & 0.961 & 0.986 & **0.990** \\ \hline \end{tabular}
\end{table} 
Table 2: Results of F-Score (\(\uparrow\)) of each method in the ShapeNet dataset. (The bold font indicates that it is the optimal result). Figure 5: Visualization results of 3D surface-generation methods under ABC dataset. (The red frames display the local comparison results of the shape). Figure 6: Visualization results of 3D surface generation methods under Famous dataset. (The red frames display the local comparison results of the shape). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **DeepSDF** & **MeshP** & **NUD** & **SALD** & **NP** & **DeepMLS** & **Present** \\ \hline Display & 0.317 & 0.069 & 0.077 & - & 0.039 & 0.012 & **0.007** \\ Lamp & 0.955 & 0.053 & 0.075 & 0.071 & 0.080 & 0.015 & **0.012** \\ Airplane & 1.043 & 0.049 & 0.076 & 0.054 & 0.008 & 0.008 & **0.006** \\ Cabinet & 0.921 & 0.112 & 0.041 & - & 0.026 & 0.024 & **0.015** \\ Vessel & 1.254 & 0.061 & 0.079 & - & 0.022 & **0.011** & 0.014 \\ Table & 0.660 & 0.076 & 0.067 & 0.066 & 0.060 & 0.014 & **0.012** \\ Chair & 0.483 & 0.071 & 0.063 & 0.061 & 0.054 & 0.027 & **0.010** \\ Sofa & 0.496 & 0.080 & 0.071 & 0.058 & 0.012 & **0.014** & 0.017 \\ Mean & 0.766 & 0.071 & 0.069 & 0.062 & 0.038 & 0.016 & **0.014** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of CD2 (\(\times\)100) (\(\downarrow\)) of each method in the ShapeNet dataset. (The bold font indicates that it is the optimal result). Compared with the ABC dataset, the Famous dataset is more complex in shape, contains more shape details, and is usually presented as an irregular surface, making it more challenging for 3D surface generation.