# Workflow Analysis for CGH Generation with Speckle Reduction and Occlusion Culling Using GPU Acceleration

## TL;DR

This paper presents a GPU-accelerated workflow for generating computer-generated holograms with reduced speckle noise and occlusion effects.

## Contribution

The work introduces a novel GPU-optimized algorithm that jointly addresses speckle noise and occlusion in CGH using temporal multiplexing.

## Key findings

- Temporal multiplexing effectively reduces speckle noise and performs occlusion culling in point clouds.
- Strict occlusion conditions show minimal impact of temporal filtering on computational cost.
- The workflow combines simulation and experimental validation for scene reconstruction.

## Abstract

Although GPUs are widely used in Computer-Generated Holography (CGH), their specific application to concrete problems such as occlusion or speckle filtering through temporal multiplexing is not yet standardized and has not been fully explored. This work aims to optimize the software architecture by taking the GPU architecture into account in a novel way for these particular tasks. We present an optimized algorithm for CGH computation that provides a joint solution to the problems of speckle noise and occlusion. The workflow includes the generation and illumination of a 3D scene, the calculation of the CGH including color, occlusion, and temporal speckle-noise filtering, followed by scene reconstruction through both simulation and experimental methods. The research focuses on implementing a temporal multiplexing technique that simultaneously performs speckle denoising and occlusion culling for point clouds, evaluating two types of occlusion that differ in whether the occlusion effect dominates over the depth effect in a scene stored in a CGH, while leveraging the parallel processing capabilities of GPUs to achieve a more immersive and high-quality visual experience. To this end, the total computational cost associated with generating color and occlusion CGHs is evaluated, quantifying the relative contribution of each factor. The results indicate that, under strict occlusion conditions, temporal multiplexing filtering does not significantly impact the overall computational cost of CGH calculation.