NeISF: Neural Incident Stokes Field for Geometry and Material Estimation
This addresses geometry and material estimation for scenes with complex light interactions like inter-reflections, representing a domain-specific advancement.
The paper tackles multi-view inverse rendering with inter-reflections by proposing Neural Incident Stokes Fields (NeISF), which uses polarization cues to reduce ambiguities; it outperforms existing methods in synthetic and real scenarios.
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.