首次实现导体与电介质的极化逆渲染，支持真实材质重建。
NeISF++: Neural Incident Stokes Field for Polarized Inverse Rendering of Conductors and Dielectrics
- 提出统一描述导体与电介质的pBRDF模型。
- 利用DoLP图像初始化几何，有效应对强镜面反射。
- 在合成与实拍数据上均优于现有方法，适合材质重建任务。
近期逆渲染方法通过利用偏振线索显著提升了形状、材质和光照的重建效果。然而，现有方法仅支持电介质，忽略了日常中广泛存在的导体。由于导体与电介质具有不同的反射特性，使用旧方法会导致明显误差。此外，导体表面光滑，易产生强镜面反射，难以重建。为此，本文提出NeISF++，一种支持导体与电介质的逆渲染框架。核心在于一个能同时描述两类材料的通用pBRDF。针对强镜面反射问题，提出基于DoLP图像的几何初始化方法，该物理线索对亮度不变，因此对强反射具有鲁棒性。在自建合成与真实数据集上的实验表明，本方法在几何与材质分解以及后续重光照等任务上均超越现有极化逆渲染方法。
原文摘要 · Abstract (English)
Recent inverse rendering methods have greatly improved shape, material, and illumination reconstruction by utilizing polarization cues. However, existing methods only support dielectrics, ignoring conductors that are found everywhere in life. Since conductors and dielectrics have different reflection properties, using previous conductor methods will lead to obvious errors. In addition, conductors are glossy, which may cause strong specular reflection and is hard to reconstruct. To solve the above issues, we propose NeISF++, an inverse rendering pipeline that supports conductors and dielectrics. The key ingredient for our proposal is a general pBRDF that describes both conductors and dielectrics. As for the strong specular reflection problem, we propose a novel geometry initialization method using DoLP images. This physical cue is invariant to intensities and thus robust to strong specular reflections. Experimental results on our synthetic and real datasets show that our method surpasses the existing polarized inverse rendering methods for geometry and material decomposition as well as downstream tasks like relighting.
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