This paper presents GIR, a 3D Gaussian Inverse Rendering method for
relightable scene factorization. Compared to existing methods leveraging
discrete meshes or neural implicit fields for inverse rendering, our method
utilizes 3D Gaussians to estimate the material properties, illumination, and
geometry of an object from multi-view images. Our study is motivated by the
evidence showing that 3D Gaussian is a more promising backbone than neural
fields in terms of performance, versatility, and efficiency. In this paper, we
aim to answer the question: ``How can 3D Gaussian be applied to improve the
performance of inverse rendering?'' To address the complexity of estimating
normals based on discrete and often in-homogeneous distributed 3D Gaussian
representations, we proposed an efficient self-regularization method that
facilitates the modeling of surface normals without the need for additional
supervision. To reconstruct indirect illumination, we propose an approach that
simulates ray tracing. Extensive experiments demonstrate our proposed GIR's
superior performance over existing methods across multiple tasks on a variety
of widely used datasets in inverse rendering. This substantiates its efficacy
and broad applicability, highlighting its potential as an influential tool in
relighting and reconstruction. Project page: this https URL

提出了一种使用 3D 高斯逆渲染的方法（GIR），用于重构可重照场景因素。通过使用 3D 高斯估计物体的材料属性、照明和几何结构，相比于现有的离散网格或神经隐式场逆渲染方法，我们的方法在性能、通用性和效率方面具备更大潜力。