## 4 Results
### Experimental Results  
The test results shown in Figure 6, and the Chamfer distance quantitative test results for both L1 and L2 normalization, including the F1-score for two thresholds as tabulated in Table 3, demonstrate that CAP-UDF is able to smoothly reconstruct the 3D point to mesh in both exterior and interior objects with 0.389 of L1-norm Chamfer distances from point cloud-to-mesh generation batch run and L2-norm Chamfer distances from mesh evaluation whereas Anchor-UDF generated a quite unpolished surface on the whole body. Furthermore, the F-score, which is a metric used to evaluate performance, was also boosted to 94.7% and 99.8% for the 0.005 and 0.01-based thresholds, respectively.  
The samples shown above in Figures 7 and 8 show the visual comparison of reconstructed objects from three perspectives over Deep Fashion 3D (PIFu pre-processed data). Each sample is represented as (a) 2D original garment, (b) ground truth, (c) ground truth (wireframe), (d) Pixel2 Mesh, (e) Pixel2 Mesh (wireframe), (f) PIFu, (g) PIFu (wireframe), (h) Anchor-UDF, (i) Anchor-UDF (wireframe), (j) CAP-UDF, and (k) CAP-UDF (wireframe), respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Studies** & **Chamfer L1** & **Chamfer L2** & **F-Score-0.005** & **F-Score-0.01** \\ \hline Raw input & 0.681 & 0.165 & 0.835 & 0.981 \\ Anchor-UDF & 0.444 & 0.099 & 0.932 & 0.997 \\ CAP-UDF & 0.389 & 0.089 & 0.947 & 0.998 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation metrics from three perspectives over ShapeNet cars.  
Figure 6: Visual comparison of reconstructed objects from three perspectives over ShapeNet cars. (**a**) Raw input; (**b**) Raw input (Wireframe); (**c**) Anchor-UDF; and (**d**) CAP–UDF.  
Figure 7: Visual comparison (Front view) of reconstructed objects from three perspectives over Deep Fashion 3D (PIFu pre-processed data) from (**a**–**k**) sample. The samples shown above in Figure 7 show the visual comparison of reconstructed objects from three perspectives over Deep Fashion 3D (PIFu pre-processed data). Each sample is represented as (**a**) 2D original garment, (**b**) ground truth, (**c**) ground truth (wireframe), (**d**) Pixel2 Mesh, (**e**) Pixel2 Mesh (wireframe), (**f**) PIFu, (**g**) PIFu (wireframe), (**h**) Anchor-UDF, (**i**) Anchor-UDF (wireframe), (**j**) CAP–UDF, and (**k**) CAP–UDF (wireframe), respectively.  
As our purpose is for mobile visualization in actual implementation, we established the visual benchmark of garment data in the same L1- and L2-norm Chamfer distance measures. The quantitative results, as indicated in Table 4, show the Chamfer distance measured by point cloud during ground truth and predicted objects that CAP-UDF is uppermost improved by down to 0.586 and 0.129 for L1 and L2 normalization, respectively. In addition, the illustration results shown in Table 4 prove that the smoothness of the point-to-mesh regenerated object connection is much better, although some points are missing when compared to previous studies. Further investigation is needed to obtain in-depth information about whether it is on the inside or outside surfaces.  
Figure 8: Visual comparison (Top view) of reconstructed objects from three perspectives over Deep Fashion 3D (PIFu pre-processed data) from (**a**–**k**) sample. Each sample represented is shown in Figure 8 as (**a**) point cloud PIFu-generating garment, (**b**) ground truth, (**c**) ground truth (wireframe), (**d**) Pixel2 Mesh, (**e**) Pixel2 Mesh (wireframe), (**f**) PIFu, (**g**) PIFu (wireframe), (**h**) Anchor-UDF, (**i**) Anchor-UDF (wireframe), (**j**) CAP–UDF, and (**k**) CAP–UDF (wireframe).