Recently developed iterative and deep learning-based approaches to computer-generated holography (CGH) have been shown to achieve high-quality photorealistic 3D images with spatial light modulators. However, such approaches remain overly cumbersome for patterning sparse collections of target points across a photoresponsive volume in applications including biological microscopy and material processing. Specifically, in addition to requiring heavy computation that cannot accommodate real-time operation in mobile or hardware-light settings, existing sampling-dependent 3D CGH methods preclude the ability to place target points with arbitrary precision, limiting accessible depths to a handful of planes. Accordingly, we present a non-iterative point cloud holography algorithm that employs fast deterministic calculations in order to efficiently allocate patches of SLM pixels to different target points in the 3D volume and spread the patterning of all points across multiple time frames. Compared to a matched-performance implementation of the iterative Gerchberg-Saxton algorithm, our algorithm's relative computation speed advantage was found to increase with SLM pixel count, exceeding 100,000x at 512x512 array format.

翻译：近期发展的基于迭代和深度学习的方法在计算机生成全息术（CGH）中已被证明能够利用空间光调制器实现高质量的光真三维图像。然而，在包括生物显微成像和材料加工等应用中，这些方法对于在光敏体积内稀疏分布的靶点图案化而言仍显得过于繁琐。具体而言，现有基于采样的三维CGH方法不仅需要大量计算而无法在移动或轻硬件条件下实现实时操作，还限制了靶点放置的任意精度，可到达深度被局限在少数几个平面上。为此，我们提出一种非迭代点云全息算法，该算法采用快速确定性计算，以高效地将SLM像素块分配给三维体积中的不同靶点，并将所有点的图案化分散到多个时间帧中。与同等性能的迭代Gerchberg-Saxton算法实现相比，我们的算法相对计算速度优势随SLM像素数增加而提升，在512×512阵列格式下超过100,000倍。