Sparse point cloud computer-generated holography with the Gabor transform

Area of Science:

• Optics and Photonics
• Computer Graphics
• Computational Imaging

Background:

• Computer-generated holography (CGH) faces computational challenges.
• Phase-added stereogram methods offer speedups but introduce artifacts and reduce accuracy.
• Existing sparse diffraction calculations lack efficiency and precision.

Purpose of the Study:

• To develop a novel, efficient, and accurate method for computer-generated holography.
• To address the computational cost limitations of traditional CGH techniques.
• To enhance the quality and speed of holographic display generation.

Main Methods:

• Integration of the Gabor transform for sparse diffraction calculations.
• Partitioning point clouds into Lozenge-shaped cells for optimized GPU memory management.
• Utilizing cache-friendly operations for enhanced computational efficiency.

Main Results:

• Achieved up to a 47× speedup compared to optimized brute-force methods.
• Demonstrated significant improvements in both speed and accuracy on large point clouds.
• Showcased enhanced visual quality compared to classic phase-added stereograms.

Conclusions:

• The novel Gabor transform-based approach offers a substantial advancement in CGH.
• Optimized memory management on GPUs leads to practical and efficient holographic rendering.
• This method effectively balances computational cost, speed, and holographic image fidelity.