6D Pose Estimation of Unseen Objects for Industrial Augmented Reality

This paper addresses the challenges of implementing markerless Augmented Reality (AR) in complex manufacturing settings. Making AR systems more intuitive, robust, and adaptable is a required step to make their adoption possible in the industry. Among the hard constraints encountered in uncontrolled, real-world environments, we notably face the dynamic nature of production lines and the evolving appearance of the objects during the assembly process. Emerging deep learning (DL) methods enable 6D object pose estimation for AR registration of moving objects. However, they need a significant amount of 6D obj ect pose ground truth data. In real-world scenarios, such a requirement cannot be fulfilled, because of two factors: the complexity of establishing an accurate 6D pose labeling procedure for large objects in a real production line and the wide variety of object states and appearances encountered along the assembly line. For this reason, it is necessary to develop alternative 6D pose estimation techniques capable of handling unseen objects. To this end, this paper introduces a novel pipeline relying on HoloLens 2 for data capture, Neural Radiance Fields (N eRF) for 3D model generation, and MegaPose for 6D pose estimation. The proposed approach enables 6D pose estimation without object-specific training or laborious pose labeling.