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Titel:

Polarimetric Pose Prediction

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Gao, Daoyi; Li, Yitong; Ruhkamp, Patrick; Skobleva, Iuliia; Wysocki, Magdalena; Jung, HyunJun; Wang, Pengyuan; Guridi, Arturo; Navab, Nassir; Busam, Benjamin
Abstract:
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...     »
Kongress- / Buchtitel:
arXiv PrePrint
Jahr:
2021
Monat:
Dec
 BibTeX