Multi-view inverse rendering is the problem of estimating the scene parameters such as shapes, materials, or illuminations from a sequence of images captured under different viewpoints. Many approaches, however, assume single light bounce and thus fail to recover challenging scenarios like inter-reflections. On the other hand, simply extending those methods to consider multi-bounced light requires more assumptions to alleviate the ambiguity. To address this problem, we propose Neural Incident Stokes Fields (NeISF), a multi-view inverse rendering framework that reduces ambiguities using polarization cues. The primary motivation for using polarization cues is that it is the accumulation of multi-bounced light, providing rich information about geometry and material. Based on this knowledge, the proposed incident Stokes field efficiently models the accumulated polarization effect with the aid of an original physically-based differentiable polarimetric renderer. Lastly, experimental results show that our method outperforms the existing works in synthetic and real scenarios.

翻译：多视角逆渲染旨在从不同视角下拍摄的图像序列中估计场景参数（如形状、材质或光照）。然而，许多方法假设单次光反射，因而难以处理互反射等复杂场景。另一方面，简单地扩展这些方法以考虑多次反射光，需要更多假设来缓解歧义性。为解决这一问题，我们提出神经入射斯托克斯场（NeISF），一种利用偏振线索减少歧义的多视角逆渲染框架。采用偏振线索的主要动机在于，它是多次反射光的累积，能够提供关于几何和材质的丰富信息。基于这一认知，所提出的入射斯托克斯场借助原创的基于物理的可微偏振渲染器，高效建模了累积偏振效应。最后，实验结果表明，我们的方法在合成场景和真实场景中均优于现有工作。