# High-Precision Depth Map Estimation from Missing Viewpoints for 360-Degree Digital Holography
## 4 Experiment Results and Discussion
### Qualitative Results of the Image Quality
As shown in Figure 17, a typical focus-defocus effect can be observed between the two objects at the same viewpoint; the accommodation effect is typically observed when holographic 3D images are reconstructed from CGHs. Figure 18 shows the _ACC_ trend of CGHs to the observer's rotation angle for unlearned amorphous rock. The individual CGH is generated using the depth map result estimated from each deep learning model. For the section where two objects do not overlap, the _ACC_ of each model shows a similar trend. However, for the section where two objects overlap (the gray boxes in Figure 18), the _ACC_ of HDD is significantly higher than that of AdaBins. Similar to the simulation results in Section 4.2.3, 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. Figure 19 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 amorphous rock, as presented in Figure 19, 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 4.3 Holographic 3D reconstruction from CGH using each set of RGB color and depth map given in Figure 4.4 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). Figure 4.5 ACC trend of CGHs generated using the depth map estimated from each model to the observation’s rotation angle for the shape of an amorphous rock. The \(x\)-axis represents the observation’s rotation angle and the \(y\)-axis represents the _ACC_ of CGH synthesized from each model. Gray-colored boxes indicate regions where two objects overlap for each 360-degree video scene. Figure 20 shows the holographic 3D reconstruction results simulated from CGHs, which were generated using the same depth map estimations as we obtained in Figure 4.6 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 objects' 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, similar to the simulation results in Section 4.2.3, 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.