Fast calculation method of computer-generated hologram using a depth camera with point cloud gridding

Zhao, Yu; Shi, Chen-Xiao; Piao, Yan; Piao, Mei-Lan; Kim, Nam

doi:10.1016/j.optcom.2017.11.040

Cited by 26 publications

(3 citation statements)

References 14 publications

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“…When the input of a is H state, the corresponding output will be assigned to the H row operator units (H-ROUs). When the input of a is V state, the corresponding output will be assigned to the V row operator units (V-ROUs) [23]- [25], each row corresponds to the output of one multiplier, and the 19-24 line LCDs have no output and shows no-light state, the experimental results are shown in Figure 7 [35].…”

Section: Experimental Tests and Resultsmentioning

confidence: 99%

Algorithm on Higher-Order Derivative Based on Ternary Optical Computer

et al. 2020

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As an important tool in the field of mathematics, higher-order derivation problems are widely used in differentials, quantum mechanics, and engineering applications. However, in the electronic computer (EC), due to the existence of the carry in the calculation, the computational efficiency is low when solving the higher-order derivation problem. In response to this problem, the ternary optical computer (TOC) has the advantages of no carry-in and the characteristics of numerous data bits, reconfigurable processors and parallel computing. Solve the higher-order derivation problems with complex operations by constructing multipliers and adders on the TOC platform, and copying multiple composite operator units (COUs). This article introduces the design of the higher-order derivative algorithm based on TOC in detail, the reconfiguration process of the multiplier and adder, and the number of bits of the multiplier and adder required in the implementation is given. Besides, the hardware resources and clock cycles in the operation are analyzed. The feasibility of the implementation scheme is verified by experiments. Compared with the traditional higherorder derivative, the higher-order derivative based on the TOC is superior in time performance, computational efficiency, and processing of complex operations. Due to the limitation of the research stage, the algorithm is only applicable to the function of polynomials, which lays a foundation for the further research of higherorder derivative algorithms, and has certain application significance.

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Section: Experimental Tests and Resultsmentioning

confidence: 99%

Algorithm on Higher-Order Derivative Based on Ternary Optical Computer

et al. 2020

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“…In order to achieve higher reconstruction quality, the author proposed a holographic display system based on a multi-depth camera [22]. The author has also used a high-quality digital single-lens reflex (DSLR) camera to capture 2D images and constructed a high-precision color holographic optical reconstruction system based on multi-digital SLR cameras to reconstruct real objects with high precision and large viewing angle [23]. Nevertheless, the research on reconstruction quality needs to be further expanded to meet the actual needs, which makes the color holographic reconstruction image more similar to the real object.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a full-color holographic system using RGB-D salient object detection

Yang

et al. 2023

Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022)

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Recently, a real objects-based full-color holographic display system usually uses a DSLR camera array or depth camera to collect data, and then relies on a spatial light modulator to modulate the input light source for the reconstruction of the 3D scene from the real objects. The main challenges faced by the holographic 3D display were introduced, including limited generation speed and accuracy of the computer-generated holograms, the imperfect performance of the holographic display system. In this research, we generated more effective and accurate point cloud data by developing a 3D saliency detection model in the acquisition module. Object points categorized into depth girds with identical depth values in the red, green, and blue (RGB) channels. In each channel, the depth girds are segmented into M × N parts, and only the effective area of the depth grids will be calculated. Computer-generated holograms (CGHs) are generated from efficient depth grids by using Fast Fourier transform (FFT). Compared to the wave-front recording plane (WRP) and traditional PCG methods, the computational complexity is dramatically reduced. The feasibility of the proposed approaches is established through experiments.

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“…Liu et al [16] proposed a new Delaunay triangulation algorithm for point clouds that divides the point cloud into triangular elements and derives inactive triangulation. Zhao et al [17] proposed a method to quickly extract the features of point clouds based on gridding. This method first establishes a virtual model of the point cloud, then enhances the normal features of the point cloud and uses fast Fourier transform (FFT) to calculate the diffraction of point clouds on the grid.…”

Section: Introductionmentioning

confidence: 99%

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

Zhao

Guo

et al. 2021

IJGI

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Point cloud data can accurately and intuitively reflect the spatial relationship between the coal wall and underground fully mechanized mining equipment. However, the indirect method of point cloud feature extraction based on deep neural networks will lose some of the spatial information of the point cloud, while the direct method will lose some of the local information of the point cloud. Therefore, we propose the use of dynamic graph convolution neural network (DGCNN) to extract the geometric features of the sphere in the point cloud of the fully mechanized mining face (FMMF) in order to obtain the position of the sphere (marker) in the point cloud of the FMMF, thus providing a direct basis for the subsequent transformation of the FMMF coordinates to the national geodetic coordinates with the sphere as the intermediate medium. Firstly, we completed the production of a diversity sphere point cloud (training set) and an FMMF point cloud (test set). Secondly, we further improved the DGCNN to enhance the effect of extracting the geometric features of the sphere in the FMMF. Finally, we compared the effect of the improved DGCNN with that of PointNet and PointNet++. The results show the correctness and feasibility of using DGCNN to extract the geometric features of point clouds in the FMMF and provide a new method for the feature extraction of point clouds in the FMMF. At the same time, the results provide a direct early guarantee for analyzing the point cloud data of the FMMF under the national geodetic coordinate system in the future. This can provide an effective basis for the straightening and inclining adjustment of scraper conveyors, and it is of great significance for the transparent, unmanned, and intelligent mining of the FMMF.

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Fast calculation method of computer-generated hologram using a depth camera with point cloud gridding

Cited by 26 publications

References 14 publications

Algorithm on Higher-Order Derivative Based on Ternary Optical Computer

Algorithm on Higher-Order Derivative Based on Ternary Optical Computer

Implementation of a full-color holographic system using RGB-D salient object detection

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

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