LiteTex-GS：用于高斯泼溅的快速轻量级纹理化
LiteTex-GS: Fast and Lightweight Texturing for Gaussian Splatting
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中文总结 AI 辅助
针对高斯泼溅纹理化计算开销大的问题，提出LiteTex-GS框架，通过紧凑表示、渐进分辨率分配、贡献度剪枝和分辨率感知更新，实现更少参数与训练时间下的高质量渲染。
中文摘要 AI 辅助
高斯泼溅已实现实时新视角合成，但其紧密耦合的几何与外观表示通常需要大量基元来重现高频纹理细节，导致巨大的内存和优化成本。近年来的纹理化2D高斯方法通过将纹理图附着到高斯基元上缓解了这一限制。然而，弥合离散高斯与连续2D网格之间的根本结构差距需要复杂的参数化，这引入了严重的计算开销。该开销从根本上损害了高斯泼溅原有的效率，使得在精细纹理化与计算敏捷性之间取得平衡成为一个尚未解决的挑战。为解决这些问题，我们提出了LiteTex-GS，一个用于高斯泼溅的快速轻量级纹理化框架。我们的方法初始化一个极其紧凑的表示，为每个高斯分配最小的局部纹理，并逐步仅向具有显著重建误差的基元分配更高分辨率。为维持精简的几何骨架，我们引入了一种基于贡献度和面积的剪枝策略，以消除低效用高斯。此外，为缓解纹理上采样引起的梯度稀释，我们设计了一种分辨率感知的更新规则，以保持快速且稳定的收敛。在标准新视角合成基准上的大量实验表明，与现有纹理化高斯基线相比，我们的方法在显著减少参数数量和训练时间的同时，实现了具有竞争力或更优的渲染质量。
英文摘要
Gaussian Splatting has enabled real-time novel view synthesis, but its tightly coupled geometry and appearance representation often require a large number of primitives to reproduce high-frequency texture details, leading to substantial memory and optimization costs. Recent textured 2D Gaussian methods alleviate this limitation by attaching texture maps to Gaussian primitives. However, bridging the fundamental structural gap between discrete Gaussians and continuous 2D grids requires complex parameterizations that introduce severe computational overhead. This overhead fundamentally compromises the original efficiency of Gaussian Splatting, making the balance between detailed texturing and computational agility an unresolved challenge. To address these challenges, we propose LiteTex-GS, a fast and lightweight texturing framework for Gaussian Splatting. Our method initializes an extremely compact representation, assigning minimal local texture to each Gaussian and progressively allocates higher resolution only to primitives with significant reconstruction errors. To maintain a streamlined geometric scaffold, we introduce a contribution- and area-aware pruning strategy that eliminates low-utility Gaussians. Furthermore, to mitigate the gradient dilution caused by texture upsampling, we design a resolution-aware update rule that preserves rapid and stable convergence. Extensive experiments on standard novel view synthesis benchmarks demonstrate that our method achieves competitive or superior rendering quality while using substantially fewer parameters and less training time than existing textured Gaussian baselines.
发表机构
- Nanchang University（南昌大学）
- Peking University（北京大学）
- Henan University（河南大学）
机构由 AI 辅助整理，请以论文原文为准。
