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Nestor is a real-time recognition and camera pose estimation system for planar shapes. The system allows shapes that carry contextual meanings for humans to be used as Augmented Reality (AR) tracking targets. The user can teach the system new shapes in real time. New shapes can be shown to the system frontally, or they can be automatically rectified according to previously learned shapes. Shapes can be automatically assigned virtual content by classification according to a shape class library. Nestor performs shape recognition by analyzing contour structures and generating projective-invariant signatures from their concavities. The concavities are further used to extract features for pose estimation and tracking. Pose refinement is carried out by minimizing the reprojection error between sample points on each image contour and its library counterpart. Sample points are matched by evolving an active contour in real time. Our experiments show that the system provides stable and accurate registration, and runs at interactive frame rates on a Nokia N95 mobile phone.

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Sketching leverages human skills for various purposes. In-Place Augmented Reality Sketching experiences build on the intuitiveness and flexibility of hand sketching for tasks like content creation. In this paper we explore the design space of InPlace Augmented Reality Sketching, with particular attention to content authoring in games. We propose a contextual model that offers a framework for the exploration of this design space by the research community. We describe a sketch-based AR racing game we developed to demonstrate the proposed model. The game is developed on top of our shape recognition and 3D registration library for mobile AR.