Polarimetric Inverse Rendering for Transparent Shapes Reconstruction

Understand

In this work, we propose a novel method for the detailed reconstruction of transparent objects by exploiting polarimetric cues.

• Most of the existing methods usually lack sufficient constraints and suffer from the over-smooth problem.
• Hence, we introduce polarization information as a complementary cue.
• We implicitly represent the object's geometry as a neural network, while the polarization render is capable of rendering the object's polarization images from the given shape and illumination configuration.