Complex-Valued 2D Gaussian Representation for Computer-Generated Holography

Complex-valued Gaussian primitives have recently been ex-plored for representing holographic radiance x001Celds in 3D novel view syn-thesis. In this work, we extend this line of research to the hologramoptimization domain and propose a structured representation based oncomplex-valued 2D Gaussian primitives. Inspired by Gabor's theory,we show that our primitive attains the minimum spacex0015frequency un-certainty and reduces the parameter search space by 5:1 compared toper-pixel parameterization. To enable end-to-end training, we develop adix001Berentiable rasterizer for our representation, integrated with a GPU-optimized light propagation kernel in free space. Extensive experimentsshow that our method reduces VRAM usage by up to 30% and acceler-ates optimization by 50% over standard autodix001B-based implementations,delivers up to 13 dB higher PSNR than prior Gaussian-based methods,and achieves up to 3200× faster rendering while maintaining reconstruc-tion quality on par with existing CGH approaches. For evaluation, weintroduce a conversion procedure that adapts our representation to prac-tical hologram formats, including smooth and random phase-only holo-grams. By reducing the hologram parameter search space, our representa-tion enables a more scalable hologram estimation in the next-generationcomputer-generated holography systems.