# High-Precision Depth Map Estimation from Missing Viewpoints for 360-Degree Digital Holography
## 5 Discussion and Conclusions  
In this paper, we proposed and demonstrated a novel CNN model that learns depth map estimation from missing viewpoints, especially well-fit for holographic 3D. The proposed model, which we call HDD, uses only _MSE_ for better depth map estimation performance in comparison with the CDD, which uses _SSIM_ 90% and _MSE_ 10% as the loss function. We designed and prepared 9832 multi-view images with a resolution of 640 \(\times\) 360. In the proposed model, HDD estimated depth maps by extracting features and up-sampling. Figure 19: Depth map estimation comparison with respect to ground truth depth map in a typical section where two objects overlap. Each of these solid objects was a new amorphous 3D rock which was not used in the course of the training phase. The red box indicates the result from the proposed model. The 3D model of each amorphous rock was provided from TurboSquid, [[https://www.turbosquid.com/ko/3d-models/cave-rock-02-base-model-1944210](https://www.turbosquid.com/ko/3d-models/cave-rock-02-base-model-1944210)]([https://www.turbosquid.com/ko/3d-models/cave-rock-02-base-model-1944210](https://www.turbosquid.com/ko/3d-models/cave-rock-02-base-model-1944210)), accessed on 17 September 2022. the data set for RGB color image and ground truth depth map 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). Figure 20: Reconstruction results from CGHs in a typical section where two objects overlap, as seen in Figure 19. The observational camera’s focus was placed on the head of the target object between the two objects reconstructed from CGH. (The red box indicates the result from the proposed model). The weights were optimized using the _MSE_ loss function. For quantitative assessment, we compared the estimated depth maps from HDD with those from CDD using the _PSNR_, _ACC_, _RMSE_. As shown in Figure 3, the proposed HDD model is numerically superior to CDD in terms of _PSNR_, _ACC_, and _RMSE_. In addition, as shown in Table 4, although HDD is numerically inferior to CDD on the metrics of absolute relative error (_Abs rel_), it is superior to CDD on the metrics of squared relative error (_Sq rel_) and _LRMSE_. To qualitatively evaluate the depth map estimation performance of the HDD, we not only compared the depth map estimation results to verify the improvement of the proposed HDD model over the CDD model, but also compared the CGH reconstruction results of these two models. Furthermore, to increase the reliability of the proposed model, we also compared the estimated depth map image quality of the holographic 3D reconstruction of our proposed model with another competitive model, AdaBins. Through the experiments of depth map estimation, CGH synthesis, and reconstruction with both objects used in the training and complicated objects not used in the training, we found out that HDD is more suitable than AdaBins in scenarios such as objects overlapping for 360-degree digital holographic movies. Furthermore, we found that HDD is superior to AdaBins and CDD in the ability to estimate depth map for each 360-degree scene regarding unlearned, more complicated objects. The contributions of this study are as follows: First, we demonstrate the ability of our proposed HDD to learn and produce depth map estimation with high accuracy from multi-view RGB images. Second, we prove the feasibility of applying deep learning-based, estimated depth maps to synthesize CGHs, with which we can quantitatively evaluate the degree of accuracy in the performance of our proposed model for holography. Third, we illustrate the effectiveness of CGHs synthesized via the proposed HDD through direct numerical/optical observations of holographic 3D images.