AI Insight
This research introduces a novel method for computer-generated holography using complex-valued 2D Gaussian primitives to represent holograms more efficiently. The approach reduces the parameter space by a factor of five compared to traditional per-pixel methods while achieving the minimum space-frequency uncertainty based on Gabor's theory. The method demonstrates significant improvements in computational efficiency, reducing memory usage by 30% and accelerating optimization by 50%, while delivering up to 13 dB higher image quality than previous Gaussian-based approaches and rendering speeds up to 3200 times faster than existing methods.
Why it matters
This advancement could enable more practical and scalable holographic display systems by dramatically reducing computational requirements while maintaining or improving image quality. The efficiency gains make real-time holographic rendering more feasible for applications in 3D visualization, augmented reality, and next-generation display technologies.
Understand the Science
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Abstract: Complex-valued Gaussian primitives have recently been explored for representing holographic radiance fields in 3D novel view synthesis. In this work, we extend this line of research to the hologram optimization domain and propose a structured representation based on complex-valued 2D Gaussian primitives. Inspired by Gabor’s theory, we show that our primitive attains the minimum space-frequency uncertainty and reduces the parameter search space by 5:1 compared to per-pixel parameterization. To enable end-to-end training, we develop a differentiable rasterizer for our representation, integrated with a GPU-optimized light propagation kernel in free space. Extensive experiments show that our method reduces VRAM usage by up to 30% and accelerates optimization by 50% over standard autodiff-based implementations, delivers up to 13 dB higher PSNR than prior Gaussian-based methods, and achieves up to 3200x faster rendering while maintaining reconstruction quality on par with existing CGH approaches. For evaluation, we introduce a conversion procedure that adapts our representation to practical hologram formats, including smooth and random phase-only holograms. By reducing the hologram parameter search space, our representation enables a more scalable hologram estimation in the next-generation computer-generated holography systems.
Source: Complex-Valued 2D Gaussian Representation for Computer-Generated Holography