Acceleration of polygon-based computer-generated holograms using look-up tables and reduction of the table size via principal component analysis

Adaptive layer-based computer-generated holograms

We propose a novel, to the best of our knowledge, and fast adaptive layer-based (ALB) method for generating a computer-generated hologram (CGH) with accurate depth information. A complex three-dimensional (3D) object is adaptively divided into layers along the depth direction according to its own non-uniformly distributed depth coordinates, which reduces the depth error caused by the conventional layer-based method. Each adaptive layer generates a single-layer hologram using the angular spectrum method for diffraction, and the final hologram of a complex three-dimensional object is obtained by superimposing all the adaptive layer holograms. A hologram derived with the proposed method is referred to as an adaptive layer-based hologram (ALBH). Our demonstration shows that the desired reconstruction can be achieved with 52 adaptive layers in 8.7 s, whereas the conventional method requires 397 layers in 74.9 s.

Efficient rendering by parallelogram-approximation for full analytical polygon-based computer-generated holography using planar texture mapping

We have developed a full analytical method with texture mapping for polygon-based computer-generated holography. A parallel planar projection mapping for holographic rendering along with affine transformation and self-similar segmentation is derived. Based on this method, we further propose a parallelogram-approximation to reduce the number of polygons used in the polygon-based technique. We demonstrate that the overall method can reduce the computational effort by 50% as compared to an existing method without sacrificing the reconstruction quality based on high precision rendering of complex textures. Numerical and optical reconstructions have shown the effectiveness of the overall scheme.

Point-polygon hybrid method for generating holograms

Computer-generated holograms (CGHs) are usually calculated from point clouds or polygon meshes. Point-based holograms are good at depicting details of objects, such as continuous depth cues, while polygon-based holograms tend to efficiently render high-density surfaces with accurate occlusions. Herein, we propose a novel point-polygon hybrid method (PPHM) to compute CGHs for the first time (to the best of our knowledge), which takes advantage of both point-based and polygon-based methods, and thus performs better than each of them separately. Reconstructions of 3D object holograms confirm that the proposed PPHM can present continuous depth cues with fewer triangles, implying high computational efficiency without losing quality.

Fast shadow casting algorithm in analytical polygon-based computer-generated holography

Shadow casting is essential in computer graphics, which can significantly enhance the reality of rendered images. However, shadow casting is rarely studied in polygon-based computer-generated holography (CGH) because state-of-art triangle-based occlusion handling methods are too complicated for shadow casting and unfeasible for complex mutual occlusion handling. We proposed a novel drawing method based on the analytical polygon-based CGH framework and achieved Z-buffer-based occlusion handling instead of the traditional Painter's algorithm. We also achieved shadow casting for parallel and point light sources. Our framework can be generalized to N-edge polygon (N-gon) rendering and accelerated using CUDA hardware, by which the rendering speed can be significantly enhanced.

Deep hologram converter from low-precision to middle-precision holograms

We propose a deep hologram converter based on deep learning to convert low-precision holograms into middle-precision holograms. The low-precision holograms were calculated using a shorter bit width. It can increase the amount of data packing for single instruction/multiple data in the software approach and the number of calculation circuits in the hardware approach. One small and one large deep neural network (DNN) are investigated. The large DNN exhibited better image quality, whereas the smaller DNN exhibited a faster inference time. Although the study demonstrated the effectiveness of point-cloud hologram calculations, this scheme could be extended to various other hologram calculation algorithms.

Wavefront recording plane-like method for polygon-based holograms

The wavefront recording plane (WRP) method is an algorithm for computer-generated holograms, which has significantly promoted the accelerated computation of point-based holograms. Similarly, in this paper, we propose a WRP-like method for polygon-based holograms. A WRP is placed near the object, and the diffracted fields of all polygons are aggregated in the WRP so that the fields propagating from the polygonal mesh affect only a small region of the plane rather than the full region. Unlike the conventional WRP method used in point-based holograms, the proposed WRP-like method utilizes sparse sampling in the frequency domain to significantly reduce the practical computational kernel size. The proposed WRP-like method and the analytical shading model are used to generate polygon-based holograms of multiple three-dimensional (3D) objects, which are then reproduced to confirm 3D perception. The results indicate that the proposed WRP-like method based on an analytical algorithm is hundreds of times faster than the reference full region sampling case; a hologram with tens of thousands of triangles can be computed in seconds even on a CPU, whereas previous methods required a graphics processing unit to achieve these speeds.

Accelerating hologram generation using oriented-separable convolution and wavefront recording planes

Recently, holographic displays have gained attention owing to their natural presentation of three-dimensional (3D) images; however, the enormous amount of computation has hindered their applicability. This study proposes an oriented-separable convolution accelerated using the wavefront-recording plane (WRP) method and recurrence formulas. We discuss the orientation of 3D objects that affects computational efficiency, which is overcome by reconsidering the orientation, and the suitability of the proposed method for hardware implementations.

Review of computer-generated hologram algorithms for color dynamic holographic three-dimensional display

Holographic three-dimensional display is an important display technique because it can provide all depth information of a real or virtual scene without any special eyewear. In recent years, with the development of computer and optoelectronic technology, computer-generated holograms have attracted extensive attention and developed as the most promising method to realize holographic display. However, some bottlenecks still restrict the development of computer-generated holograms, such as heavy computation burden, low image quality, and the complicated system of color holographic display. To overcome these problems, numerous algorithms have been investigated with the aim of color dynamic holographic three-dimensional display. In this review, we will explain the essence of various computer-generated hologram algorithms and provide some insights for future research.

Variable-intensity line 3D images drawn using kinoform-type electroholography superimposed with phase error

Three-dimensional (3D) display using electroholography is a promising technology for next-generation television systems; however, its applicability is limited by the heavy computational load for obtaining computer-generated holograms (CGHs). The CG-line method is an algorithm that calculates CGHs to display 3D line-drawn objects at a very high computational speed but with limited expressiveness; for instance, the intensity along the line must be constant. Herein, we propose an extension for drawing gradated 3D lines using the CG-line method by superimposing phase noise. Consequently, we succeeded in drawing gradated 3D lines while maintaining the high computational speed of the original CG-line method.

Fast 3D Analytical Affine Transformation for Polygon-Based Computer-Generated Holograms

We present a fast 3D analytical affine transformation (F3DAAT) method to obtain polygon-based computer-generated holograms (CGHs). CGHs consisting of tens of thousands of triangles from 3D objects are obtained by this method. We have attempted a revised method based on previous 3D affine transformation methods. In order to improve computational efficiency, we have derived and analyzed our proposed affine transformation matrix. We show that we have further increased the computational efficiency compared with previous affine methods. We also have added flat shading to improve the reconstructed image quality. A 3D object from a 3D camera is reconstructed holographically by numerical and optical experiments.