Recently, 3D Gaussian Splatting (3D-GS) has achieved impressive results in novel view synthesis, demonstrating high fidelity and efficiency. However, it easily exhibits needle-like artifacts, especially when increasing the sampling rate. Mip-Splatting tries to remove these artifacts with a 3D smoothing filter for frequency constraints and a 2D Mip filter for approximated supersampling. Unfortunately, it tends to produce over-blurred results, and sometimes needle-like Gaussians still persist. Our spectral analysis of the covariance matrix during optimization and densification reveals that current 3D-GS lacks shape awareness, relying instead on spectral radius and view positional gradients to determine splitting. As a result, needle-like Gaussians with small positional gradients and low spectral entropy fail to split and overfit high-frequency details. Furthermore, both the filters used in 3D-GS and Mip-Splatting reduce the spectral entropy and increase the condition number during zooming in to synthesize novel view, causing view inconsistencies and more pronounced artifacts. Our Spectral-GS, based on spectral analysis, introduces 3D shape-aware splitting and 2D view-consistent filtering strategies, effectively addressing these issues, enhancing 3D-GS's capability to represent high-frequency details without noticeable artifacts, and achieving high-quality photorealistic rendering.

翻译：近年来，三维高斯泼溅（3D-GS）在新视角合成领域取得了令人瞩目的成果，展现出高保真度与高效率。然而，该方法容易产生针状伪影，尤其在提高采样率时更为明显。Mip-Splatting尝试通过用于频率约束的三维平滑滤波器和用于近似超采样的二维Mip滤波器来消除这些伪影。遗憾的是，该方法往往会产生过度模糊的结果，且针状高斯分布有时依然存在。我们对优化与致密化过程中协方差矩阵的谱分析表明，当前3D-GS缺乏形状感知能力，仅依赖谱半径和视角位置梯度来决定分裂操作。这导致具有小位置梯度和低谱熵的针状高斯分布无法分裂，并过度拟合高频细节。此外，3D-GS和Mip-Splatting中使用的滤波器在放大合成新视角时会降低谱熵并增大条件数，从而引发视角不一致性和更显著的伪影。我们基于谱分析提出的Spectral-GS，引入了三维形状感知分裂策略和二维视角一致滤波策略，有效解决了上述问题，增强了3D-GS在无明显伪影情况下表征高频细节的能力，实现了高质量的光照真实感渲染。