# High-Precision Depth Map Estimation from Missing Viewpoints for 360-Degree Digital Holography
## 4 Experiment Results and Discussion
### Qualitative Results of the Image Quality
The data set for the ground truth depth map is 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). Figure 7: Comparison of numerical 3D reconstruction images from CGHs based on estimated depth maps for the torus. (Each focused object is marked by an arrow. The red box indicates the result from the proposed HDD model. Numerical 3D reconstruction results with other objects can be seen in Figure S4). the \(ACC\) trend of CGHs to the observer's rotation angles for the torus, where the individual CGH is generated using the depth map result estimated from each deep learning model. There are some sections where AdaBins' CGH \(ACC\) is slightly higher than that of HDD. These sections are mainly located in zones where the two objects do not overlap. However, there are other sections where the CGH \(ACC\) of HDD is higher than that of AdaBins. These sections are mainly located in the regions where the two objects overlap (gray-colored boxes in Figure 8). We first checked the depth map estimation results of the HDD and AdaBins models. We then monitored the holographic 3D reconstruction results to determine the different accuracies of the CGHs generated from these two models in two different scenarios. Figures 9 and 10 show the depth map estimation results and the CGH reconstruction results for a typical section where two objects overlap, respectively. When observing the overlapped sections in each case for the torus, cube, and cone, as shown in Figures 9 and S6, we find that the boundary between the front and rear objects is neat in the depth estimation result of HDD, whereas it is uneven in the depth estimation result of AdaBins. In addition, the depth estimation result of AdaBins shows that the area of the front object (brighter area) invades the area of the rear object (darker area). This indicates that the HDD model performs both near- and far-distance estimation tasks well without a shape distortion problem, whereas the AdaBins model estimates the object located at a farther distance to be located at a closer distance. Furthermore, we observed that the AdaBins' inaccuracy of the depth map estimation near the boundary region overlapping between the front and back objects is again confirmed through the image quality test of a holographic 3D reconstruction. Figure 10 shows the holographic 3D reconstruction results simulated within the typical section in which the two objects overlap. In the case of either a front-point or rear-point focused reconstruction, the overlapped profile in Figure 10 shows that the shape-spreading/distortion phenomenon is more pronounced in 3D reconstruction results based on the estimated depth map of AdaBins in comparison with those based on the estimated depth map of HDD. In 360-degree holographic video content, which represents dynamic movements among various objects, the capability to express the exact boundary between two objects is critical to holographic 3D image quality. In this aspect, although AdaBins is finely superior to HDD when the objects do not overlap, it is not suitable in a scenario where they overlap for 360-degree digital holographic movies because the AdaBins model does not accurately perform the depth map estimation task. Conversely, it can be seen that the HDD model estimates that depth map information--to a reasonable extent--provides general video content in scenarios even where two objects do not overlap. Overall, we found that HDD is  
\[H(x,y)=L_{1}(x,y)e^{i0}+L_{2}(x,y)e^{i\pi/2}+L_{3}(x,y)e^{i\pi}+L_{4}(x,y)e^{i3 \pi/2} \tag{2}\]0 ACC trend of CGHs generated using the estimated depth map of each model to the observer’s rotation angles for the torus. The \(x\)-axis represents the observation angle and the \(y\)-axis represents the \(ACC\) of CGH. Gray-colored boxes indicate regions where two objects overlap for each 360-degree video scene.