Polarimetric Information for Multi-Modal 6D Pose Estimation of
  Photometrically Challenging Objects with Limited Data
Patrick Ruhkamp Daoyi Gao HyunJun Jung Nassir Navab Benjamin Busam
6D pose estimation pipelines that rely on RGB-only or RGB-D data show
limitations for photometrically challenging objects with e.g. textureless
surfaces, reflections or transparency. A supervised learning-based method
utilising complementary polarisation information as input modality is proposed
to overcome such limitations. This supervised approach is then extended to a
self-supervised paradigm by leveraging physical characteristics of polarised
light, thus eliminating the need for annotated real data. The methods achieve
significant advancements in pose estimation by leveraging geometric information
from polarised light and incorporating shape priors and invertible physical
constraints.