  This paper proposes the first self-supervised 6D object pose prediction from
multimodal RGB+polarimetric images. The novel training paradigm comprises 1) a
physical model to extract geometric information of polarized light, 2) a
teacher-student knowledge distillation scheme and 3) a self-supervised loss
formulation through differentiable rendering and an invertible physical
constraint. Both networks leverage the physical properties of polarized light
to learn robust geometric representations by encoding shape priors and
polarization characteristics derived from our physical model. Geometric
pseudo-labels from the teacher support the student network without the need for
annotated real data. Dense appearance and geometric information of objects are
obtained through a differentiable renderer with the predicted pose for
self-supervised direct coupling. The student network additionally features our
proposed invertible formulation of the physical shape priors that enables
end-to-end self-supervised training through physical constraints of derived
polarization characteristics compared against polarimetric input images. We
specifically focus on photometrically challenging objects with texture-less or
reflective surfaces and transparent materials for which the most prominent
performance gain is reported.
