让纹理采样随视觉复杂度动态调整，提升2D高斯点云渲染质量。
FACT-GS: Frequency-Aligned Complexity-Aware Texture Reparameterization for 2D Gaussian Splatting
- 根据局部视觉频率动态分配纹理采样密度，替代均匀采样。
- 相同参数量下，高频细节更清晰，渲染质量显著提升。
- 适合追求高质量实时渲染的3D重建与视觉生成研究者。
真实场景外观建模因高斯点阵技术快速发展，实现了实时、高质量渲染。近期工作引入每个高斯单元的局部纹理，增强了表达能力。但现有方法对每个高斯使用统一的采样网格，忽略局部视觉复杂度差异，导致纹理空间利用率低。本文提出FACT-GS框架，依据局部视觉频率自适应分配纹理采样密度。基于自适应采样理论，将纹理参数化重构为可微分的采样密度分配问题，通过变形场的雅可比矩阵调节局部采样密度，实现非均匀采样。在固定分辨率纹理网格上操作，保持实时性能的同时，在相同参数预算下恢复更锐利的高频细节。
原文摘要 · Abstract (English)
Realistic scene appearance modeling has advanced rapidly with Gaussian Splatting, which enables real-time, high-quality rendering. Recent advances introduced per-primitive textures that incorporate spatial color variations within each Gaussian, improving their expressiveness. However, texture-based Gaussians parameterize appearance with a uniform per-Gaussian sampling grid, allocating equal sampling density regardless of local visual complexity, which leads to inefficient texture space utilization. We introduce FACT-GS, a Frequency-Aligned Complexity-aware Texture Gaussian Splatting framework that allocates texture sampling density according to local visual frequency. Grounded in adaptive sampling theory, FACT-GS reformulates texture parameterization as a differentiable sampling-density allocation problem, replacing the uniform textures with a learnable frequency-aware allocation strategy implemented via a deformation field whose Jacobian modulates local sampling density. Built on 2D Gaussian Splatting, FACT-GS performs non-uniform sampling on fixed-resolution texture grids, preserving real-time performance while recovering sharper high-frequency details under the same parameter budget.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。