FACT-GS: Frequency-Aligned Complexity-Aware Texture Reparameterization for 2D Gaussian Splatting

TL;DR

FACT-GS introduces a frequency-aware texture sampling method for Gaussian Splatting that allocates resources based on local visual complexity, enhancing detail without sacrificing real-time rendering.

Contribution

It proposes a novel differentiable, frequency-aligned texture parameterization that improves visual detail efficiency in 2D Gaussian Splatting.

Findings

Sharper high-frequency details achieved with same parameter budget.

Non-uniform sampling improves texture efficiency and visual quality.

Framework maintains real-time performance despite adaptive sampling.

Abstract

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.