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.
最近，3D Gaussian Splatting (3D-GS) 在新视角合成方面取得了令人印象深刻的成果，展示了高保真度和高效率。然而，当采样率增加时，3D-GS 容易出现针状伪影。Mip-Splatting 试图通过3D平滑滤波器来进行频率约束，并使用2D Mip滤波器实现近似的超采样，从而消除这些伪影。不幸的是，它往往会产生过度模糊的结果，有时针状高斯分布仍然存在。我们对协方差矩阵在优化和致密化过程中的频谱分析表明，当前的3D-GS缺乏对形状的感知，而是依赖频谱半径和视角位置梯度来决定分裂。因此，位置梯度较小且频谱熵低的针状高斯分布无法分裂，过拟合高频细节。此外，3D-GS 和 Mip-Splatting 中使用的滤波器在缩放以合成新视角时，降低了频谱熵并增加了条件数，导致视角不一致以及更明显的伪影。我们的Spectral-GS基于频谱分析，引入了3D形状感知分裂和2D视角一致的滤波策略，有效解决了这些问题，增强了3D-GS在表示高频细节时的能力，避免了显著的伪影，从而实现高质量的真实感渲染。