  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.
