# Holographic 3D Display Using Depth Maps Generated by 2D-to-3D Rendering Approach
## 1 Introduction  
Holography is a technology which can build mathematical and physical connections between targets and holographic fringes. Thus, it has been widely employed in the fields of 3D imaging and 3D display. In the field of 3D imaging, captured holographic fringes are often employed to reconstruct corresponding targets [1; 2; 3]. Applications of holographic imaging include sonar [4], radar [5], microscopy [6], et al. In the field of 3D display, holographic fringes are often calculated from targets by algorithms [7; 8; 9]. As the holographic display can provide all the depth cues that human eyes are capable of perceiving, it is considered a promising option for 3D display [10; 11; 12; 13]. It has the potential to be utilized in many augmented reality (AR) application scenarios, including video education [14; 15], spatial cognition and navigation [16], and health sciences [17].  
Currently, the shortage of 3D content limits the application of holographic 3D displays. Three-dimensional acquisition devices, including light-field cameras [18] and time-of-flight (TOF) cameras [19], are regarded as the solution to produce 3D content. For light-field cameras, the quality of image reduces with the shooting distance [20]. The additional processes that are required to address this issue [21] increase system complexity. For most TOF cameras, the resolution is insufficient, leading to lower quality displays with limited definition. In addition, the production of 3D content by 3D acquisition devices is expensive and hardware intensive. Furthermore, existing 2D content cannot be fully utilized in 3D acquisition devices.  
2D-to-3D rendering provides an alternative option to enrich 3D content. Various features, including edge [22; 23], texture [22; 24], color [25], and motion [24; 26], have been used to calculate computer-generated depth maps (CGDMs) from 2D images. However, a 2D-to-3D rendering approach that uses only one of these features may not be widely applicable. Therefore, 2D-to-3D rendering approaches that use mixed features have beenemployed [27, 28, 29]. The CGDMs calculated by the mixed-features-based method are more stable than that by single-feature-based method. Currently, most 2D-to-3D rendering approaches are utilized for spatial cognition and image identification. The CGDMs are usually not optimized for holographic algorithms.  
With the development of machine learning technology, learning-based 2D-to-3D rendering approaches have also been widely employed to enrich 3D content [30, 31, 32, 33, 34]. Learning-based approaches utilize deep neural network to generate CGDMs of 2D images, which have the advantages of strong ability of generalization and high accuracy of depth estimation. However, learning-based approaches need tons of data for training. Obtaining reliable 2D/3D data pairs is a challenging task for current learning-based approaches.  
In this study, we present a 2D-to-3D rendering approach with mixed features. Based on features, 2D images are first classified into three categories, including the distant view, perspective, and close-up types. The CGDM for each category is obtained by using a corresponding model. The obtained CGDMs have been optimized for the layer-based holographic algorithm and can be applied directly to calculate the computer-generated holograms (CGHs) of the 2D images. The resulting CGHs provide 3D reconstructions with prominent depth variations.