### Research of Our Paper  
In order to speed up the calculation, scholars have conducted a lot of research. The authors once proposed a multi-camera holographic system based on heterogeneous sampled two-dimensional images and compressed point cloud grid (C-PCG). Compared with the traditional wavefront recording plane method, the quality of the recon-structed images is significantly improved and the computational complexity is greatly reduced [21]. In order to improve the quality of reconstruction, the author also proposed a holographic system based on multi-depth camera, which uses efficient depth grid to represent real three-dimensional objects, so as to flexibly and effectively obtain full-color reconstruction images [22]. [PERSON] et al., have proposed an angular-spectrum layer-oriented method to generate CGHs of 3D scenes [23]. Moreover, a novel layer-based CGH calculation using layer classification and occlusion culling for the 3D object was proposed by [PERSON] et al. [24]. However, neither of these approaches is oriented towards the actual objects. They are limited to computer-synthesized images of virtual 3D objects. Our method is based on the point cloud grid algorithm for actual objects, which actually adds the transformation from point cloud to layer. The kernel is layer to layer transformation, and uses the Fourier transform similar to that of the WRP method. Our method is optimized for the resolution of the holograms required for the transformation from point cloud to [PERSON] for optical reconstruction.  
Based on the hybrid Taylor Rayleigh-Sommerfeld diffraction algorithm proposed by [PERSON] et al., and the author's previous research on full-color holographic systems, we propose a method called Taylor Rayleigh-Sommerfeld point cloud grid (TR-PCG) for Taylor expansion of the Rayleigh-Sommerfeld diffraction to reduce the computational complexity based on a full-color holographic display system in this paper. The method proposed by [PERSON] can only be used for 2D image reconstruction and quality improvement. We took her ideas and made improvements based on our original PCG approach, which effectively increased speed while maintaining quality. Our approach is primarily based on 3D reconstruction of real objects. We first process the collected point cloud grid by dividing the red, green, and blue channels, then combine fast Fourier transform (FFT) with Taylor expansion for the radial values in the convolution, which retains the quadratic term. Meanwhile, we modify the data type, which means changing the double-precision floating-point number to a single-precision floating-point number, thus obtaining similar numerical accuracy in a less-bit format. Under the guarantee of sufficient reconstruction quality, our method significantly improves the reconstruction speed by about 20%. We have verified the effectiveness of the algorithm through simulation experiments. It not only overcomes the deficiency that the reconstructed images of some acceleration algorithms are limited to monochrome images, but also effectively improves the calculation speed based on ensuring the reconstruction quality, and the resolution is improved to 2048 \(\times\) 2048. We speed up the calculation speed of the hologram generation stage and ensure the quality of the full-color reconstructed images, which further improves the performance of the system.