2DGH: 二维高斯-埃尔米特泼溅用于高质量渲染和更好的几何特征
2DGH: 2D Gaussian-Hermite Splatting for High-quality Rendering and Better Geometry Features
AI总结：
提出高斯-埃尔米特核作为新基元，增强各向异性和变形能力，在几何重建和新视角合成上优于传统高斯泼溅。
摘要：
二维高斯泼溅最近成为3D重建中的重要方法，能够同时实现新视角合成和几何重建。虽然众所周知的高斯核被广泛使用，但其缺乏各向异性和变形能力导致物体轮廓边缘暗淡模糊，限制了当前高斯泼溅方法的重建质量。为了增强表示能力，我们从量子物理学中汲取灵感，提出使用高斯-埃尔米特核作为高斯泼溅中的新基元。新核采用统一的数学形式，并扩展了高斯函数，后者作为更新后一般公式中的零阶特例。我们的实验表明，所提出的高斯-埃尔米特核在几何重建和新视角合成任务上均优于传统高斯泼溅核。具体而言，在DTU数据集上，我们的方法实现了更精确的几何重建，而在MipNeRF360和我们定制的Detail数据集上，在新视角合成中取得了更好的结果。这些结果凸显了高斯-埃尔米特核在高质量3D重建和渲染中的潜力。
英文摘要：
2D Gaussian Splatting has recently emerged as a significant method in 3D reconstruction, enabling novel view synthesis and geometry reconstruction simultaneously. While the well-known Gaussian kernel is broadly used, its lack of anisotropy and deformation ability leads to dim and vague edges at object silhouettes, limiting the reconstruction quality of current Gaussian splatting methods. To enhance the representation power, we draw inspiration from quantum physics and propose to use the Gaussian-Hermite kernel as the new primitive in Gaussian splatting. The new kernel takes a unified mathematical form and extends the Gaussian function, which serves as the zero-rank special case in the updated general formulation. Our experiments demonstrate that the proposed Gaussian-Hermite kernel achieves improved performance over traditional Gaussian Splatting kernels on both geometry reconstruction and novel-view synthesis tasks. Specifically, on the DTU dataset, our method yields more accurate geometry reconstruction, while on datasets such as MipNeRF360 and our customized Detail dataset, it achieves better results in novel-view synthesis. These results highlight the potential of the Gaussian-Hermite kernel for high-quality 3D reconstruction and rendering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
