# High-Precision Depth Map Estimation from Missing Viewpoints for 360-Degree Digital Holography
## 4 Experiment Results and Discussion
### Qualitative Results of the Image Quality
(Results of the other objects can be seen in Figure S5). superior to AdaBins in terms of conserving the very detailed original shape information within the region where two objects overlap, as demonstrated in Figures 9 and 10. From the experiment, we concluded that the deep learning-based HDD model proposed in the study can reasonably provide an adequate quality of depth map estimation, which can be used to generate CGH for 360-degree holographic video content, especially without noticeable shape distortion of the holographically reconstructed 3D image. 2.3 Comparison of the Proposed Model (HDD) with Previous Models (Using Two Kinds of Complicated Objects Not Used in the Course of the Training Phase)  
Unlike the previous experiment performed in Section 4.2.2, we present another experiment of depth map estimation performed using new, complicated objects that are not used in the course of the training phase so that we can demonstrate the superiority of the proposed model (HDD) over AdaBins and CDD. In addition, we synthesized CGH with the estimated depth map and then reconstructed a holographic 3D image to compare the depth map estimation performance of the proposed model with the performance of previous models. For this experiment, we chose an icosahedron and a dodecahedron as the solid figures, which are more complicated than the four kinds of objects used for the training phase. RGB color images of these two objects not used in the training process were used as input data for each model and then the corresponding depth map images were  
\[H(x,y)=L_{1}(x,y)e^{i0}+L_{2}(x,y)e^{i\pi/2}+L_{3}(x,y)e^{i\pi}+L_{4}(x,y)e^{i3 \pi/2} \tag{2}\]1 CGH’s reconstruction results for a typical section where two objects overlap. (The red box indicates the result from the proposed model. Results of the other objects can be seen in Figure S7). In the case of AdaBins, it is observed that the shape of the holographically reconstructed object is distorted because of the severe inaccuracy of depth map estimation by the AdaBins model near the boundary regions between objects. \[H(x,y)=L_{1}(x,y)e^{i0}+L_{2}(x,y)e^{i\pi/2}+L_{3}(x,y)e^{i\pi}+L_{4}(x,y)e^{i3 \pi/2} \tag{2}\]2 Depth map estimation comparison to the ground truth depth map for a typical section where two objects overlap. The red box indicates the result from the proposed model. Results of the other objects can be seen in Figure S6. Data set for RGB color image and 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). estimated from each model. The depth maps, as shown in Figure 11, were estimated under the same conditions as the weights of each model which had been obtained in the course of the training phase based on the four pre-existing objects (torus, cube, cone, and sphere). In Figure 11, it can be visually confirmed that the edge expressions in individual objects from HDD or CDD are more accurate than the results from AdaBins for unlearned, complicated objects. To compare the results of HDD with those of CDD, we calculated values of the _PSNR_ for these two objects (dodecahedron and icosahedron): the _PSNR_ of HDD is 73.02 dB for icosahedron and 72.96 dB for dodecahedron, while the _PSNR_ of CDD is 70.25 dB for icosahedron and 71.23 dB for dodecahedron. Figure 12 shows the 3D images reconstructed from CGH for ground truth, HDD, AdaBins, and CDD. As shown in Figure 12, 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 13 shows the _ACC_ trend of CGHs to the observer's rotation angle for the unlearned icosahedron. The individual CGH is generated using the depth map result estimated from each deep learning model.