# High-Precision Depth Map Estimation from Missing Viewpoints for 360-Degree Digital Holography
## 4 Experiment Results and Discussion
### Qualitative Results of the Image Quality
However, the photographs to monitor reconstructed scenes in Figure 4 indicate that this actual deviation is small enough to well support the accommodation effect on holographic 3D scenes for both deep learning and ground truth cases; when an object between two objects exists on the camera's focus, its photographic image is clearly sharp, while an object that is out of focus is completely blurred. Results to observe reconstructed 3D scenes in the other objects are presented in Figure S2. 2.2 Comparison of the Proposed Model (HDD) with Previous Models (Using Four Kinds of Objects Used in the Course of the Training Phase)  
To qualitatively evaluate the depth map estimation performance of the HDD, we not only compared the depth map estimations to verify the improvement of the proposed HDD model over the CDD model (reference model), but we also compared the CGH reconstruction results of these two models. Furthermore, to increase the reliability of the proposed HDD model, we also compared the estimated depth map/image quality of the holographic 3D reconstruction of our proposed model with another model called AdaBins (state-of-the-art model), which was recently developed by [PERSON] et al. [31]. In this experimental verification, we included the results of the _ACC_ (accuracy test) of CGH images. The AdaBins model, as a competitive model, was also trained using the same training data set that we used for both HDD and CDD models. We mainly focused on  
Figure 5: Geometry of the optical reconstruction system for holographic 3D observation: (**a**) schematic diagram to be used in numerical simulation, and (**b**) its optical experiment setup. Observer’s eye lens in (**a**) is located at the position of the focal length of the field lens, corresponding to the observation position of the Fourier plane away from holographic display (LCoS) in (**b**). comparing depth map estimation results and CGH's reconstruction results from HDD with those from AdaBins. Figure 6 shows the depth map estimation results from HDD, AdaBins, and CDD, along with the ground truth depth map for the torus. Figure 7 shows the 3D images reconstructed from CGH for ground truth, HDD, AdaBins, and CDD. When we move the observer's focal point from one object to another for each given scene, as shown in Figure 7, a typical focus-defocus effect is clearly observed between the two objects at the same viewpoint. This effect manifests the accommodation by observing the holographic 3D images reconstructed from CGHs. To quantitatively compare the quality of CGH from the estimated depth map with that of CGH from the ground truth depth map, we carried out a performance evaluation using the \(ACC\), which is defined as  
\[ACC=\frac{\sum_{r,g,b}(I\cdot I^{\prime})}{\sqrt{\left[\sum_{r,g,b}I^{2}\right] \left[\sum_{r,g,b}I^{\prime 2}\right]}} \tag{3}\]  
where \(I\) is the brightness of each color in the CGH image obtained using RGB image and the depth map, estimated by each deep learning model, and \(I^{\prime}\) is the brightness of each color in the CGH image obtained using RGB image and the ground truth depth map. When the estimation result and ground truth are identical, or \(I=KI^{\prime}\) (\(k\) is a positive scalar), \(ACC=1\). When a mismatch occurs between them, \(0\leq ACC\leq 1\). We synthesized and reconstructed CGHs using the depth maps estimated from HDD/AdaBins/CDD and the ground truth. For these three types of models, Figure 8 shows  
Figure 6: Comparison of depth map estimations for the torus. The red box indicates the result from the proposed HDD model. Information close to the camera are represented as brighter, and information far to the camera are represented as darker. Results of depth map estimations with other objects can be seen in Figure S3.