Detection and Pose Estimation of flat, Texture-less Industry Objects on
  HoloLens using synthetic Training
Thomas Pöllabauer Fabian Rücker Andreas Franek Felix Gorschlüter
Current state-of-the-art 6d pose estimation is too compute intensive to be
deployed on edge devices, such as Microsoft HoloLens (2) or Apple iPad, both
used for an increasing number of augmented reality applications. The quality of
AR is greatly dependent on its capabilities to detect and overlay geometry
within the scene. We propose a synthetically trained client-server-based
augmented reality application, demonstrating state-of-the-art object pose
estimation of metallic and texture-less industry objects on edge devices.
Synthetic data enables training without real photographs, i.e. for
yet-to-be-manufactured objects. Our qualitative evaluation on an AR-assisted
sorting task, and quantitative evaluation on both renderings, as well as
real-world data recorded on HoloLens 2, sheds light on its real-world
applicability.