Ultra-high precision motion sensing leveraging computer vision (CV) is a key technology in many high-precision AR/VR applications such as precise industrial manufacture and image-guided surgery, yet conventional CV can be challenged by moiré-based sensing mechanism, thanks to moiré pattern's high sensitivity to six degrees of freedom (6-DoF) pose changes. Unfortunately, existing moiré-based solutions, in their infancy, cannot deal with complicated curvilinear moiré patterns caused by various perspective angles. In this paper, we propose a generalized moiré-based mechanism, MoiréVision, towards practical adoptions; it relies on high-frequency gratings as visual marker to help extract the fine-grained feature points for ultra-high precision motion sensing. As the foundation of general moiré-based sensing, we propose a formulation to characterize "uncontrolled" curvilinear moiré patterns in practical scenarios. To deal with the problem of moiré feature interference in practice, we propose a Gabor-based algorithm to separate overlapped curvilinear moiré patterns from two dimensions. Furthermore, to extract fine-grained feature points for high-precision motion sensing, we propose a bending function-based model and a resolution-enhanced strategy to reconstruct detailed texture of moiré markers and extract moiré feature points at sub-pixel level. Extensive experimental results show that MoiréVision greatly enhances the usability and generalizability of moiré-based sensing systems in real-world applications.

MoiréVision: A Generalized Moiré-based Mechanism for 6-DoF Motion Sensing