Recent inverse rendering methods have improved shape, material, and illumination reconstruction using polarization cues. However, they only support dielectrics, ignoring conductors, which are common in everyday life. Since conductors and dielectrics have different reflection properties, using previous dielectrics-based 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.

Multi-view inverse rendering is the problem of estimating the scene parameters such as shapes, materials, or il-luminations from a sequence of images captured under dif-ferent viewpoints. Many approaches, however, assume single light bounce and thus fail to recover challenging sce-narios like inter-reflections. On the other hand, simply ex-tending those methods to consider multi-bounced light re-quires 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 pri-mary motivation for using polarization cues is that it is the accumulation of multi-bounced light, providing rich infor-mation about geometry and material. Based on this knowl-edge, the proposed incident Stokes field efficiently models the accumulated polarization effect with the aid of an orig-inal physically-based differentiable polarimetric renderer. Lastly, experimental results show that our method outper-forms the existing works in synthetic and real scenarios.

In this paper, we describe a supervised four-dimensional (4D) light field segmentation method that uses a graph-cut algorithm. Since 4D light field data has implicit depth information and contains redundancy, it differs from simple 4D hyper-volume. In order to preserve redundancy, we define two neighboring ray types (spatial and angular) in light field data. To obtain higher segmentation accuracy, we also design a learning-based likelihood, called objectness, which utilizes appearance and disparity cues. We show the effectiveness of our method via numerical evaluation and some light field editing applications using both synthetic and real-world light fields.