Publikationen

Autor	Pöllabauer, Thomas; Rücker, Fabian; Franek, Andreas; Gorschlüter, Felix
Datum	2023
Art	Conference Paper
Abstrakt	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.
Konferenz	Scandinavian Conference on Image Analysis 2023
ISBN	978-3-031-31437-7
Publisher	Springer
Url	https://publica.fraunhofer.de/handle/publica/441201

Detection and Pose Estimation of Flat, Texture-Less Industry Objects on HoloLens Using Synthetic Training