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

Area of Science:

• Computer Science
• Optics
• Computer Graphics

Background:

• GPUs are prevalent in Computer-Generated Holography (CGH) but lack standardized methods for specific challenges like speckle noise and occlusion.
• Temporal multiplexing is a technique with potential for CGH but requires further exploration for joint speckle and occlusion handling.

Purpose of the Study:

• To optimize CGH computation by novelly integrating GPU architecture for speckle filtering and occlusion culling.
• To present an algorithm that jointly solves speckle noise and occlusion problems in CGH.
• To evaluate the computational cost and effectiveness of temporal multiplexing for these tasks.

Main Methods:

• Developed an optimized algorithm for CGH computation leveraging GPU parallel processing.
• Implemented a temporal multiplexing technique for simultaneous speckle denoising and occlusion culling on point clouds.
• Evaluated two occlusion types and analyzed computational costs for color and occlusion CGH generation.

Main Results:

• The proposed algorithm successfully integrates speckle filtering and occlusion culling within the CGH workflow.
• Temporal multiplexing filtering showed minimal impact on overall computational cost under strict occlusion conditions.
• The research validates the feasibility of joint speckle and occlusion handling in CGH using GPUs.

Conclusions:

• Optimized CGH software architecture by considering GPU specifics for enhanced performance.
• Temporal multiplexing is an effective strategy for simultaneous speckle denoising and occlusion culling in CGH.
• Achieved high-quality visual experience in CGH reconstruction through GPU acceleration and optimized algorithms.