GlossGau: Efficient Inverse Rendering for Glossy Surface with Anisotropic Spherical Gaussian

TL;DR

GlossGau is an efficient inverse rendering method that accurately reconstructs glossy surfaces by modeling surface normals, BRDF, and incident lights using Anisotropic Spherical Gaussian, while maintaining real-time training and rendering speeds.

Contribution

It introduces a novel framework combining ASG with 2D Gaussian Splatting for fast, high-quality glossy surface reconstruction, overcoming limitations of previous methods.

Findings

Achieves competitive or superior reconstruction quality on glossy datasets.

Reduces optimization time compared to previous GS-based methods.

Maintains training and rendering speeds comparable to vanilla 3D-GS.

Abstract

The reconstruction of 3D objects from calibrated photographs represents a fundamental yet intricate challenge in the domains of computer graphics and vision. Although neural reconstruction approaches based on Neural Radiance Fields (NeRF) have shown remarkable capabilities, their processing costs remain substantial. Recently, the advent of 3D Gaussian Splatting (3D-GS) largely improves the training efficiency and facilitates to generate realistic rendering in real-time. However, due to the limited ability of Spherical Harmonics (SH) to represent high-frequency information, 3D-GS falls short in reconstructing glossy objects. Researchers have turned to enhance the specular expressiveness of 3D-GS through inverse rendering. Yet these methods often struggle to maintain the training and rendering efficiency, undermining the benefits of Gaussian Splatting techniques. In this paper, we introduce GlossGau, an efficient inverse rendering framework that reconstructs scenes with glossy surfaces while maintaining training and rendering speeds comparable to vanilla 3D-GS. Specifically, we explicitly model the surface normals, Bidirectional Reflectance Distribution Function (BRDF) parameters, as well as incident lights and use Anisotropic Spherical Gaussian (ASG) to approximate the per-Gaussian Normal Distribution Function under the microfacet model. We utilize 2D Gaussian Splatting (2D-GS) as foundational primitives and apply regularization to significantly alleviate the normal estimation challenge encountered in related works. Experiments demonstrate that GlossGau achieves competitive or superior reconstruction on datasets with glossy surfaces. Compared with previous GS-based works that address the specular surface, our optimization time is considerably less.