Detection and Pose Estimation of flat, Texture-less Industry Objects on HoloLens using synthetic Training
This addresses the need for efficient AR object detection and pose estimation on edge devices, particularly for industrial applications, though it appears incremental as it builds on existing synthetic training methods.
The paper tackles the problem of compute-intensive 6D pose estimation for edge devices like HoloLens by proposing a synthetically trained client-server AR application, achieving state-of-the-art pose estimation for metallic and texture-less industrial objects, with evaluations on both synthetic and real-world data.
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.