Biblio

While existing visual recognition approaches, which rely on 2D images to train their underlying models, work well for object classification, recognizing the changing state of a 3D object requires addressing several additional challenges. This paper proposes an active visual recognition approach to this problem, leveraging camera pose data available on mobile devices. With this approach, the state of a 3D object, which captures its appearance changes, can be recognized in real time. Our novel approach selects informative video frames filtered by 6-DOF camera poses to train a deep learning model to recognize object state. We validate our approach through a prototype for Augmented Reality-assisted hardware maintenance.