Polarimetric Pose Prediction

Light has many properties that can be passively mea- sured by vision sensors. Colour-band separated wavelength and intensity are arguably the most commonly used ones for monocular 6D object pose estimation. This paper explores how complementary polarisation information, i.e. the ori- entation of light wave oscillations, can influence the accu- racy of pose predictions. A hybrid model that leverages physical priors jointly with a data-driven learning strat- egy is designed and carefully tested on objects with dif- ferent amount of photometric complexity. Our design not only significantly improves the pose accuracy in relation to photometric state-of-the-art approaches, but also enables object pose estimation for highly reflective and transparent objects.     «

Light has many properties that can be passively mea- sured by vision sensors. Colour-band separated wavelength and intensity are arguably the most commonly used ones for monocular 6D object pose estimation. This paper explores how complementary polarisation information, i.e. the ori- entation of light wave oscillations, can influence the accu- racy of pose predictions. A hybrid model that leverages physical priors jointly with a data-driven learning strat- egy is designed and carefully tested on...     »