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.
这篇论文介绍了一种名为GIR的3D高斯逆渲染方法，用于可重照明的场景分解。与现有利用离散网格或神经隐式场进行逆渲染的方法相比，我们的方法使用3D高斯来估计从多视图图像中物体的材料属性、照明和几何形状。我们的研究是由证据激发的，这些证据表明，3D高斯作为骨干网络在性能、多功能性和效率方面比神经场更有前景。在本文中，我们旨在回答这样一个问题：“3D高斯如何应用于提高逆渲染的性能？”为了解决基于离散且通常是不均匀分布的3D高斯表示估计法线的复杂性，我们提出了一种有效的自我调节方法，该方法有助于在不需要额外监督的情况下对表面法线进行建模。为了重建间接照明，我们提出了一种模拟光线追踪的方法。大量实验表明，我们提出的GIR在多项任务上的性能优于现有方法，这些任务在逆渲染中广泛使用的各种数据集上进行。这证实了其有效性和广泛的适用性，突出了其作为重照明和重建中有影响力工具的潜力。