用复数二维高斯基元优化全息图，大幅降低显存占用并提速。
Complex-Valued 2D Gaussian Representation for Computer-Generated Holography
- 基于复数2D高斯基元构建结构化全息表示，减少5倍参数搜索空间。
- 相比传统方法，显存降低30%，优化加速50%，峰值信噪比提升13 dB。
- 适合需要高效全息生成的下一代显示系统，尤其擅长快速渲染。
复数高斯基元近期被用于三维新视角合成中的全息辐射场表示。本文将其拓展至全息图优化领域，提出基于复数二维高斯基元的结构化表示。受盖博理论启发，该基元实现最小时空频不确定性，相比逐像素参数化减少5倍参数搜索空间。为支持端到端训练，开发了可微分光栅化器，并集成GPU优化的自由空间光传播核。大量实验表明，本方法显存使用最多降低30%，优化速度提升50%，相比现有高斯基方法峰值信噪比最高提升13 dB，渲染速度最快达3200倍提升，同时保持与现有计算机生成全息（CGH）方法相当的重建质量。评估中引入转换流程，将表示适配至实际全息格式，包括平滑和随机相位纯全息图。通过缩小参数搜索空间，该表示为下一代CGH系统提供更可扩展的全息估计方案。
原文摘要 · Abstract (English)
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.
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