Abstract
Computer-generated holography (CGH) is a notoriously difficult computation problem, simulating numerical diffraction, where every scene point can affect every hologram pixel. To tackle this challenge, specialized software instructions and hardware solutions are developed to significantly reduce calculation time and power consumption. In this work, we propose a novel algorithm for high-performance point-based CGH, leveraging fixed-point integer representations, the separability of the Fresnel transform and using new look-up table free cosine representation. We report up to a 3-fold speed up over an optimized floating-point GPU implementation, as well as a 15 dB increase in quality over a state-of-the-art FPGA-based fixed-point integer solution.
Highlights
Electro-holographic displays are a promising technology for immersive 3D displays, thanks to their ability to fully reproduce the wavefield of light, thereby accounting for all human visual cues [1,2]: continuous motion parallax, and no accommodation-vergence conflict, all while supporting accurate shading and occlusion cues
One major challenge for realizing those display systems is computational, because holograms are computed by modeling numerical diffraction: every point in the virtual 3D scene creates spherical waves that can affect every hologram pixel
This many-to-many mapping, combined with the large needed resolutions and low frame rates required for driving holographic video displays, makes the use of efficient algorithms and hardware solutions a necessity
Summary
Electro-holographic displays are a promising technology for immersive 3D displays, thanks to their ability to fully reproduce the wavefield of light, thereby accounting for all human visual cues [1,2]: continuous motion parallax, and no accommodation-vergence conflict, all while supporting accurate shading and occlusion cues. One major challenge for realizing those display systems is computational, because holograms are computed by modeling numerical diffraction: every point in the virtual 3D scene creates spherical waves that can affect every hologram pixel. This many-to-many mapping, combined with the large needed resolutions and low frame rates required for driving holographic video displays, makes the use of efficient algorithms and hardware solutions a necessity. These can be combined with acceleration structures, such as wavefront recording planes [12], sparse bases [13,14] and deep neural networks [15]
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