Reconstructing objects from posed images is a crucial and complex task in computer graphics and computer vision. While NeRF-based neural reconstruction methods have exhibited impressive reconstruction ability, they tend to be time-comsuming. Recent strategies have adopted 3D Gaussian Splatting (3D-GS) for inverse rendering, which have led to quick and effective outcomes. However, these techniques generally have difficulty in producing believable geometries and materials for glossy objects, a challenge that stems from the inherent ambiguities of inverse rendering. To address this, we introduce GlossyGS, an innovative 3D-GS-based inverse rendering framework that aims to precisely reconstruct the geometry and materials of glossy objects by integrating material priors. The key idea is the use of micro-facet geometry segmentation prior, which helps to reduce the intrinsic ambiguities and improve the decomposition of geometries and materials. Additionally, we introduce a normal map prefiltering strategy to more accurately simulate the normal distribution of reflective surfaces. These strategies are integrated into a hybrid geometry and material representation that employs both explicit and implicit methods to depict glossy objects. We demonstrate through quantitative analysis and qualitative visualization that the proposed method is effective to reconstruct high-fidelity geometries and materials of glossy objects, and performs favorably against state-of-the-arts.
从有姿态的图像重建物体是计算机图形学和计算机视觉中的一项关键且复杂的任务。虽然基于NeRF的神经重建方法展示了令人印象深刻的重建能力，但这些方法往往耗时较长。最近的策略采用了3D高斯散射（3D-GS）进行逆向渲染，取得了快速且有效的成果。然而，这些技术通常难以为光滑物体生成逼真的几何形状和材质，主要是由于逆向渲染中的固有模糊性。为了解决这一问题，我们提出了GlossyGS，这是一个创新的基于3D-GS的逆向渲染框架，旨在通过整合材质先验精确重建光滑物体的几何形状和材质。其核心思想是使用微面几何分割先验，帮助减少固有模糊性，并改进几何形状和材质的分解。此外，我们引入了法线图预过滤策略，以更准确地模拟反射表面的法线分布。这些策略集成到一个混合几何和材质表示中，结合了显式和隐式方法来描绘光滑物体。通过定量分析和定性可视化，我们证明了所提出的方法在重建光滑物体的高保真几何形状和材质方面是有效的，并且相较于现有的最先进方法表现优越。