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.

User identification from hand images only is still a challenging task. In this paper, we propose a new biometric identification system based solely on a skin patch from a multispectral image. The system is utilizing a novel modified 3D CNN architecture which is taking advantage of multispectral data. We demonstrate the application of our system for the example of human identification from multispectral images of hands. To the best of our knowledge, this paper is the first to describe a pose-invariant and robust to overlapping real-time human identification system using hands. Additionally, we provide a framework to optimize the required spectral bands for the given spatial resolution limitations.