  This paper presents a learning-based method for transparent surface
estimation from a single view polarization image. Existing shape from
polarization(SfP) methods have the difficulty in estimating transparent shape
since the inherent transmission interference heavily reduces the reliability of
physics-based prior. To address this challenge, we propose the concept of
physics-based prior, which is inspired by the characteristic that the
transmission component in the polarization image has more noise than
reflection. The confidence is used to determine the contribution of the
interfered physics-based prior. Then, we build a network(TransSfP) with
multi-branch architecture to avoid the destruction of relationships between
different hierarchical inputs. To train and test our method, we construct a
dataset for transparent shape from polarization with paired polarization images
and ground-truth normal maps. Extensive experiments and comparisons demonstrate
the superior accuracy of our method.
