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：近期开发的基于迭代和深度学习的方法在计算机生成全息术领域已被证实能够利用空间光调制器实现高质量、逼真的三维图像。然而，此类方法在生物显微成像和材料加工等应用中，对跨越光响应体积的稀疏目标点集合进行图案化处理时仍过于繁琐。具体而言，现有基于采样的三维计算全息方法不仅需要繁重计算而无法适应移动或轻硬件场景下的实时操作，还无法以任意精度放置目标点，并将可达到的深度限制在少数平面上。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，将空间光调制器像素块高效分配至三维体积中的不同目标点，并将所有点的图案化处理分散至多个时间帧中。与性能匹配的迭代Gerchberg-Saxton算法实现相比，我们的算法相对计算速度优势随空间光调制器像素数增加而提升，在512×512阵列格式下超过100,000倍。