# Robust and High-Fidelity 3D Face Reconstruction Using Multiple RGB-D Cameras
## 4 Experiments
### Qualitative Evaluation  
In this section, we qualitatively evaluate the methods above and the 3D face reconstruction capability of our system on the real dataset we captured.  
Figure 6 shows a comparison of our point cloud filtering method with other filtering methods; in this experiment, we first performed the same depth filtering on all the method groups to filter out the approximate face results, and then conducted the comparison experiment. It is obvious from the comparison results that our filtering algorithm is excellent in removing edge noise. The statistical filtering excessively filters out the data that originally belonged to the face based on the core idea that the characteristics of apparent noise are sparsely distributed in space. The distribution of edge noise is not in isolated areas but closely attached to the edge area of the reconstructed subject, making the statistical filtering unable to distinguish the edge noise and remove it effectively. Radius filtering is better than the other two filtering algorithms, except for our method, in terms of results. However, radius filtering is also to determine whether the point is noise by its outlier; so, it still cannot effectively remove the edge noise attached to the face. Voxel filtering is used for noise removal by downsampling and is also ineffective for edge noise. Compared with the other methods, the point cloud after voxel filtering is the most sparse. Our fast two-stage filtering method does not consider the distribution characteristics of the edge noise but accurately segments the face region (including the edges) through human matting on the RGB image. It then combines the connection between the 2D RGB image and the 3D point cloud to make an accurate guide for point cloud filtering, thus achieving excellent performance.  
Figure 7 shows a comparison of the results of our multi-view texture alignment method for the real image set, including the original data and the comparison results of the generated facial model before and after the method was adopted. For the left image set in Figure 7, the overall color of the face captured in the right view is darker and more reddish than the other views, and after the multi-view texture alignment, the right face is closer to the middle face than before. Even in an extreme case where the hues of the right image set in Figure 7 are completely inconsistent, our texture alignment method is still able to ensure uniform face color from all views. This is very important for texture fusion.  
Figure 5: Reconstruction accuracy of our method on synthetic data with different noise levels.  
Figure 8 shows the results of our face reconstruction on the real data acquired by RealSense D435, which shows that our method can accurately recover the 3D facial model of the acquisition users with excellent results for both geometry and texture.  
The shape reconstruction details can be seen in Figure 9, from which we can see that we have clearly recovered the geometric shape of facial features even for slight differences in depth. Note that all of these results were achieved automatically without any user intervention, and our data acquisition was performed in a single moment, which makes for a good user experience and practical applications. In summary, our approach performs face reconstruction tasks with high standards for geometry and visual performance, and it can be extended to other tasks using arbitrary depth sensors for arbitrary subject reconstruction. At the same time, our algorithm maintains high efficiency to meet the requirements of practical applications while relying only on a CPU. The current system maintained an average time of 30 s on a machine equipped with an Intel Core i7 2.80 GHz CPU to complete the entire 3D face reconstruction process described above.  
Figure 6: Comparison of point cloud filtering results under different camera views. The red box contains the noise that the other filtering method failed to filter out.  
Figure 7: Multi-view texture alignment results.