Fast non-iterative algorithm for 3D point-cloud holography

TL;DR

This paper introduces a fast, non-iterative algorithm for 3D point-cloud holography that significantly outperforms traditional iterative methods in computation speed, enabling real-time applications in microscopy and material processing.

Contribution

The paper presents a novel deterministic algorithm for 3D holography that is faster and more scalable than existing iterative approaches, allowing arbitrary point placement and real-time operation.

Findings

Over 100,000x faster than iterative methods at 512x512 resolution

Achieves high-quality 3D point cloud holography with deterministic calculations

Enables real-time patterning in applications like microscopy and material processing

Abstract

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.