Bing Zhou
ABSTRACT
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.
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.Google Scholar
Cross Ref
- Steven J Henderson and Steven K Feiner. 2007. Augmented reality for maintenance and repair (armar). Technical Report. Columbia Univ New York Dept of Computer Science.Google Scholar
- Apple Inc. 2018. Apple Developer Documentation. https://developer.apple.com/documentation .Google Scholar
- Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller. 2015. Multi-view convolutional neural networks for 3d shape recognition. In Proceedings of the IEEE international conference on computer vision. 945--953. Google ScholarDigital Library
Index Terms
- Pose-assisted Active Visual Recognition in Mobile Augmented Reality
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