Detection and Pose Estimation of flat, Texture-less Industry Objects on HoloLens using synthetic Training

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 …

apple applications art augmented reality capabilities compute cs.ai cs.cv current detection devices edge edge devices industry ipad microsoft objects quality reality state synthetic texture training