ASAP-Textured Gaussians: Enhancing Textured Gaussians with Adaptive Sampling and Anisotropic Parameterization

TL;DR

This paper introduces ASAP-Textured Gaussians, a method that improves textured Gaussian rendering by adaptively sampling and anisotropically parameterizing textures, leading to better quality with fewer parameters.

Contribution

It proposes adaptive sampling and error-driven anisotropic parameterization to enhance textured Gaussian methods, reducing memory usage while maintaining high rendering quality.

Findings

Achieves high-fidelity rendering with fewer texture parameters.

Significantly improves quality-efficiency tradeoff.

Addresses inefficiencies in existing textured Gaussian approaches.

Abstract

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.