3D Gaussian Splatting has emerged as a powerful scene representation for real-time novel-view synthesis. However, its standard adaptive density control relies on screen-space positional gradients, which do not distinguish between geometric misplacement and frequency aliasing, often leading to either over-blurred high-frequency textures or inefficient over-densification. We present a structure-aware densification framework. Our key insight is that the decision to subdivide a Gaussian should be driven by an explicit comparison between its projected screen-space extent and the local structure of the texture it seeks to represent. We introduce a multi-scale frequency analysis combining structure tensors with Laplacian scale space analysis to estimate the dominant frequency at each pixel, enabling robust supervision across varying texture scales. Based on this analysis, we define $η$, a per-Gaussian, per-axis frequency violation metric that indicates when a primitive may be under-resolving local texture details. Unlike methods that perform isotropic splitting (e.g., splitting each Gaussian into two smaller ones with uniform shape), our approach performs anisotropic splitting. For each axis with high $η$, we compute a split factor to better resolve the local frequency content. We further introduce a multiview consistency criterion that aggregates $η$ observations across multiple views. By performing densification early and faster, we skip the lengthy iterative densification phases required by baseline methods and achieve significantly faster convergence. Experiments on standard benchmarks demonstrate that our method also achieves superior reconstruction quality, particularly in high-frequency regions.

翻译：3D高斯泼溅已成为实时新视角合成的一种强大场景表示方法。然而，其标准自适应密度控制依赖屏幕空间位置梯度，该方法无法区分几何错位与频率混叠，常导致高频纹理过度模糊或低效过度密化。我们提出一种结构感知密化框架。核心洞见在于：细分高斯体的决策应基于其投影到屏幕空间的延展范围与待表征纹理局部结构之间的显式比较。我们引入结合结构张量与拉普拉斯尺度空间分析的多尺度频率分析方法，以估计每个像素处的优势频率，从而实现对不同纹理尺度的稳健监督。基于该分析，我们定义η——一个逐高斯体、逐轴的频率违反度量，用于指示图元可能未能充分解析局部纹理细节的情况。与采用各向同性分割（例如每个高斯体均匀分裂为两个更小的）的方法不同，我们的方法执行各向异性分割：对每个η值高的轴计算分裂因子，以更好地解析局部频率内容。我们进一步引入多视角一致性准则，聚合来自多个视角的η观测值。通过更早、更快地进行密化，我们跳过了基线方法所需的长迭代密化阶段，实现了显著加快的收敛速度。在标准基准上的实验表明，我们的方法还能达到更优的重建质量，尤其在高频区域表现突出。