Textured Gaussians for Enhanced 3D Scene Appearance Modeling (2411.18625v2)

Abstract: 3D Gaussian Splatting (3DGS) has recently emerged as a state-of-the-art 3D reconstruction and rendering technique due to its high-quality results and fast training and rendering time. However, pixels covered by the same Gaussian are always shaded in the same color up to a Gaussian falloff scaling factor. Furthermore, the finest geometric detail any individual Gaussian can represent is a simple ellipsoid. These properties of 3DGS greatly limit the expressivity of individual Gaussian primitives. To address these issues, we draw inspiration from texture and alpha mapping in traditional graphics and integrate it with 3DGS. Specifically, we propose a new generalized Gaussian appearance representation that augments each Gaussian with alpha~(A), RGB, or RGBA texture maps to model spatially varying color and opacity across the extent of each Gaussian. As such, each Gaussian can represent a richer set of texture patterns and geometric structures, instead of just a single color and ellipsoid as in naive Gaussian Splatting. Surprisingly, we found that the expressivity of Gaussians can be greatly improved by using alpha-only texture maps, and further augmenting Gaussians with RGB texture maps achieves the highest expressivity. We validate our method on a wide variety of standard benchmark datasets and our own custom captures at both the object and scene levels. We demonstrate image quality improvements over existing methods while using a similar or lower number of Gaussians.

• The paper introduces texture maps that significantly enhance the expressive power of 3D Gaussian Splatting for detailed scene modeling.
• It employs a two-stage optimization, first fitting Gaussian parameters then refining textures to capture high-frequency appearance details.
• Experimental results demonstrate notable PSNR improvements, especially with low-density Gaussian distributions, validating the approach.

Textured Gaussians for Enhanced 3D Scene Appearance Modeling: An Expert Overview

The paper "Textured Gaussians for Enhanced 3D Scene Appearance Modeling" addresses limitations in 3D Gaussian Splatting (3DGS) by introducing textured Gaussians as a novel approach to improve 3D scene appearance modeling. The authors enhance the expressive power of 3DGS by augmenting each Gaussian with alpha, RGB, or RGBA texture maps to model spatially varying color and opacity across the Gaussian's extent, leading to superior novel-view synthesis performance.

Background and Motivation

3D Gaussian Splatting has gained prominence for 3D reconstruction and rendering due to its high-quality results and efficiency in training and rendering. However, the conventional 3DGS technique restricts the appearance representation of Gaussians to perturbations in color and geometry defined by a simple ellipsoid and uniform color scheme, limiting the method's ability to represent intricate textures and detailed appearances.

To address these limitations, the paper draws inspiration from traditional graphics methodologies like texture and alpha mapping. By integrating these concepts with 3DGS, the authors propose a texture-augmented representation that enriches each Gaussian's capability to model diverse geometric structures and texture patterns.

Methodology

The core of the proposed methodology involves augmenting each Gaussian primitive with texture maps that allow spatially varying color and opacity modeling. The textures transform the appearance capabilities of Gaussians from simple ellipsoids with a singular color to more complex shapes with high-frequency texture details. This is achieved by:

1. Texture Maps Integration: Each Gaussian is associated with a texture map (alpha-only, RGB, or RGBA) corresponding to spatially varying attributes. This addition lets Gaussians represent detailed texture patterns and dynamic opacity variations.
2. Rendering Pipeline: The textured Gaussian model incorporates ray-Gaussian intersection and texture mapping, leveraging custom CUDA kernels to efficiently render complex scenes by tracing rays through Gaussian-augmented textured spaces.
3. Two-Stage Optimization: The training process begins with a standard 3DGS optimization to prefit Gaussian parameters, followed by a texture refinement phase where texture maps are optimized to capture fine-grained appearance details.

Results and Analysis

The proposed method was validated on both standard benchmark datasets and custom-captured scenes, demonstrating notable improvements in rendering quality over existing techniques:

• The paper highlights significant improvements in PSNR on various datasets, with textured Gaussians outperforming 3DGS by a substantial margin, especially when the density of Gaussians is low.
• The modulation of color via the texture maps enables textured Gaussians to reconstruct high-frequency details and complex textures that 3DGS fails to model accurately.
• An ablation paper further confirms the utility of alpha-only textures, showing comparable results to RGBA textures and outperforming RGB textures due to spatially varying composition capabilities.

Implications and Future Work

This enhancement in Gaussian expressivity bridges a critical gap in appearance modeling, enabling realistic novel-view synthesis through more detailed texture representation. The authors suggest potential extensions in exploring local 3D volume textures or utilizing factorized representations like TensoRF or triplane for more comprehensive multidimensional texture mappings.

Future work could delve into optimizations for texture parameterizations, enhancing memory efficiency, or integrating advanced rendering tasks like specular reflections within the textured Gaussian framework.

Overall, this paper contributes a significant advancement in neural rendering by expanding the expressivity and capability of Gaussian primitives, paving the way for more complex and detailed 3D scene reconstructions.