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.

翻译：当前最先进的6D姿态估计计算量过大，难以部署在Microsoft HoloLens（第二代）或Apple iPad等边缘设备上，而这些设备正越来越多地应用于增强现实场景。AR的质量在很大程度上取决于其在场景中检测和叠加几何结构的能力。我们提出了一种基于合成训练的客户端-服务器架构增强现实应用，在边缘设备上实现了对金属及无纹理工业物体的最先进姿态估计。合成数据使无需真实照片即可进行训练，例如针对尚未制造的物体。基于AR辅助分拣任务的定性评估，以及基于渲染结果和HoloLens 2真实场景记录数据的定量评估，揭示了该方法的实际应用可行性。