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

2025-12-15

AI Summary

Existing texture Gaussian methods suffer from two key bottlenecks: (1) defining textures in canonical space leads to inefficient sampling, and (2) uniformly allocating texture parameters across all Gaussians ignores their visual importance, causing redundancy and excessive memory overhead. This paper proposes an adaptive texture Gaussian splatting framework. It introduces, for the first time, a Gaussian-density-guided adaptive sampling strategy and an error-driven anisotropic texture parameterization mechanism to enable on-demand, dynamic allocation of texture resources. By integrating density-aware sampling, rendering-error-feedback optimization, anisotropic UV mapping, and texture resource scheduling, our method achieves high-fidelity rendering while reducing texture parameter count by up to 67% and improving PSNR by 2.1 dB—significantly alleviating the efficiency–quality trade-off.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Addresses inefficient texture sampling in canonical space
• Resolves uniform texture parameterization across all Gaussians
• Improves memory efficiency for high-fidelity 3D rendering

Innovation

• Adaptive sampling based on Gaussian density distribution
• Anisotropic parameterization driven by rendering error
• Reducing texture parameters while maintaining high-fidelity rendering