通过自适应采样与异向参数化，让3D高斯点更省资源地呈现细节。
ASAP-Textured Gaussians: Enhancing Textured Gaussians with Adaptive Sampling and Anisotropic Parameterization
- 根据密度分布动态采样，只在重要区域用纹理。
- 按渲染误差分配纹理参数，复杂区域多给、简单区域少给。
- 同等质量下纹理参数减少超60%，适合部署资源受限场景。
近期进展为3D高斯点阵引入了纹理参数化，以捕捉空间变化属性，提升了外观建模与下游任务表现。然而，新增的纹理参数带来显著内存效率挑战。本文不提出新纹理形式，而是回溯现有方法，发现两大共性问题：(1) 纹理通常定义在标准空间，导致采样效率低下，纹理容量被浪费在贡献低的区域；(2) 所有高斯点统一分配纹理参数，无视其视觉复杂度，造成过度参数化。为此，本文提出两项简单但有效策略：基于高斯密度分布的自适应采样，以及基于误差驱动的异向参数化，按渲染误差动态分配纹理资源。所提ASAP Textured Gaussians显著优化了质量与效率的权衡，在大幅减少纹理参数的同时实现高保真渲染。
原文摘要 · Abstract (English)
Recent advances have equipped 3D Gaussian Splatting with texture parameterizations to capture spatially varying attributes, improving the performance of both appearance modeling and downstream tasks. However, the added texture parameters introduce significant memory efficiency challenges. Rather than proposing new texture formulations, we take a step back to examine the characteristics of existing textured Gaussian methods and identify two key limitations in common: (1) Textures are typically defined in canonical space, leading to inefficient sampling that wastes textures' capacity on low-contribution regions; and (2) texture parameterization is uniformly assigned across all Gaussians, regardless of their visual complexity, resulting in over-parameterization. In this work, we address these issues through two simple yet effective strategies: adaptive sampling based on the Gaussian density distribution and error-driven anisotropic parameterization that allocates texture resources according to rendering error. Our proposed ASAP Textured Gaussians, short for Adaptive Sampling and Anisotropic Parameterization, significantly improve the quality efficiency tradeoff, achieving high-fidelity rendering with far fewer texture parameters.
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。