# High-Precision Depth Map Estimation from Missing Viewpoints for 360-Degree Digital Holography
## 4 Experiment Results and Discussion
### Qualitative Results of the Image Quality
For the section where two objects do not overlap, the _ACC_ of each model shows a similar trend. However, some local spots exist (ranging from angles 72\({}^{\circ}\) to 90\({}^{\circ}\), and 216\({}^{\circ}\) to 234\({}^{\circ}\) in Figure 13) in which the _ACC_ of Adabins is significantly lower than that of HDD. Moreover, for the section where two objects overlap (the gray boxes in Figure 13), the _ACC_ of HDD is higher than that of AdaBins. From the _ACC_ test of CGHs in the case of unlearned, complicated objects, we found that the _ACC_ trend of CGHs that originate from AdaBins shows inconsistent behavior during a cycle of the 360-degree scenes, and the AdaBins model is inferior to the HDD model in the _ACC_ of CGHs within the regions in which two objects overlap. \[H(x,y)=L_{1}(x,y)e^{i0}+L_{2}(x,y)e^{i\pi/2}+L_{3}(x,y)e^{i\pi}+L_{4}(x,y)e^{i3 \pi/2} \tag{2}\]3 Depth map estimation results for icosahedron, which was not used in the course of the training phase. The red box indicates the result from the proposed model. Results of the dodecahedron can be seen in Figure S8. The data set for RGB color image and ground truth depth map for a pair of these solid objects was created using Autodesk Maya 2018, Maya Software ([[https://www.autodesk.com/products/maya/overview](https://www.autodesk.com/products/maya/overview)]([https://www.autodesk.com/products/maya/overview](https://www.autodesk.com/products/maya/overview)), accessed on 17 September 2022). \[H(x,y)=L_{1}(x,y)e^{i0}+L_{2}(x,y)e^{i\pi/2}+L_{3}(x,y)e^{i\pi}+L_{4}(x,y)e^{i3 \pi/2} \tag{2}\]4 Holographic 3D reconstruction from CGH using each set of RGB color and depth map given in \[H(x,y)=L_{1}(x,y)e^{i0}+L_{2}(x,y)e^{i\pi/2}+L_{3}(x,y)e^{i\pi}+L_{4}(x,y)e^{i3 \pi/2} \tag{2}\]5 The focused object between two objects to confirm the accommodation effect is marked by an arrow. (The red box indicates the result from the proposed model. Results of the dodecahedron can be seen in Figure S9). Figure 14 shows the depth map estimation results from HDD and from AdaBins for a typical case of the section where two objects overlap. From the shapes of the icosahedron, as presented in Figure 14, we observe that near the boundary region between the front and rear objects, the depth map estimation quality of HDD is much better than that of AdaBins. This result indicates that the proposed HDD model carries out both near- and far-distance estimation tasks from the camera well without a distorted shape. Figure 15 shows the holographic 3D reconstruction results simulated from CGHs, which were generated using the same depth map estimations as we obtained in \[H(x,y)=L_{1}(x,y)e^{i0}+L_{2}(x,y)e^{i\pi/2}+L_{3}(x,y)e^{i\pi}+L_{4}(x,y)e^{i3 \pi/2} \tag{2}\]6 In the case of either a front-point or rear-point focused reconstruction, it is observed that the shape distortion in the case of AdaBins is more conspicuous than that of an HDD. The reason is that the inaccuracy of depth map estimation by the AdaBins model becomes more severe near the boundary region between objects as the object's shape becomes more complicated. In conclusion, through the experiments of depth map estimation, CGH synthesis, and reconstruction with complicated objects not used in the training phase, we found out that the deep learning-based proposed model (HDD) is superior to AdaBins and CDD in the ability to estimate depth map for each 360-degree scene regarding unlearned, more complicated objects. \[H(x,y)=L_{1}(x,y)e^{i0}+L_{2}(x,y)e^{i\pi/2}+L_{3}(x,y)e^{i\pi}+L_{4}(x,y)e^{i3 \pi/2} \tag{2}\]7 Depth map estimation comparison with respect to the ground truth depth map in a typical section where two objects overlap. Each of these solid objects was an icosahedron, of which was not used in the course of the training phase. The red box indicates the result from the proposed model. Results of the dodecahedron can be seen in Figure S11.