# FCN-Based 3D Reconstruction with Multi-Source Photometric Stereo
## 4 Discussion  
### Compared with the Classic Multi-Source PS  
In some classic multi-source Photometric Stereo 3D reconstruction methods, the effect of highlights on the results is removed by a selecting method--that is, some images that contain severe highlight reflections will not participate in the calculation. However, this loses a lot of meaningful information contained in the highlight position, even reducing the rationality of the prediction. The characteristics of the neural network determined that it could be biased towards learning information from the input that was more relavent to the correct results. Therefore, using the neural network to learn will not lose the useful information of the highlight position itself, but can also help to reduce inaccurate predictions caused by specular reflections and noise. Furthermore, we represented the optimized target as the depth value of each pixel. Compared with other representations such as point clouds or voxel grids, such 2D representations make the computational cost of our network less.  
In order to verify the practical significance of the neural network used to learn Photometric Stereo for 3D reconstruction, we compared the results of the classic multi-source Photometric Stereo method (BASELINE) and ours with GT to conduct a qualitative analysis. We reconstructed the target surface with the BASELINE method using 5, 10 and 20 two-dimensional images taken under the illumination of light sources with equal angle intervals, as shown in Figure 5c-e. The BASELINE method had an obvious effect on the reconstruction of the corrugates which excessive smoothly, but the cast shadow and attached shadow caused by the fracture led to an anomaly in the 3D information extraction at the deeper fractures (Sample 3). However, there was a smooth transition in ours at the fracture site, which made our prediction more reasonable. For the defect surface with more small cracks, ours  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **rms** & **rel** & **\(\delta\)(1.25)** & **\(\delta\)(1.56)** & **\(\delta\)(1.95)** \\ \hline L2 Norm & 0.3770 & 0.3552 & 0.6492 & 0.9859 & 0.9906 \\ Ours & 0.2797 & 0.2473 & 0.2359 & 0.9727 & 0.9937 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation of our method in comparison to the reference method using the L2 norm. Lower is better for rms and rel and higher is better for \(\delta\)(t).  
could not reproduce all the details perfectly. In comparison, the BASELINE results lost more surface information, and the smooth inclined position with little feature information could not present a reasonable three-dimensional shape. In addition, because the surface features of the target samples did not conform to the standard Lambert model, the reflection around the defect resulted in different degrees of bulge in the transition from the plane to the defect in the reconstruction surface of the BASELINE method (Sample 2). Our method took GT as the direct optimization goal, which could minimize the influence of the highlights in the input on the correct prediction results.