State-Aware Configuration Detection for Augmented Reality Step-by-Step Tutorials

Presenting tutorials in augmented reality is a compelling application area, but previous attempts have been limited to objects with only a small numbers of parts. Scaling augmented reality tutorials to complex assemblies of a large number of parts is difficult, because it requires automatically discriminating many similar-looking object configurations, which poses a challenge for current object detection techniques. In this paper, we seek to lift this limitation. Our approach is inspired by the observation that, even though the number of assembly steps may be large, their order is typically highly restricted: Some actions can only be performed after others. To leverage this observation, we enhance a state-of-the-art object detector to predict the current assembly state by conditioning on the previous one, and to learn the constraints on consecutive states. This learned 'consecutive state prior' helps the detector disambiguate configurations that are otherwise too similar in terms of visual appearance to be reliably discriminated. Via the state prior, the detector is also able to improve the estimated probabilities that a state detection is correct. We experimentally demonstrate that our technique enhances the detection accuracy for assembly sequences with a large number of steps and on a variety of use cases, including furniture, Lego and origami. Additionally, we demonstrate the use of our algorithm in an interactive augmented reality application.