Deep representation of industrial components using simulated images

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In this paper, we present a visual learning framework to retrieve a 3D model and estimate its pose from a single image. To increase the quantity and quality of training data, we define our simulation space in the near infrared (NIR) band, and utilize the quasi-Monte Carlo (MC) method for scalable photorealistic rendering of manufactured components. Two types of convolutional neural network (CNN) architectures are trained over these synthetic data and a relatively small amount of real data. The first CNN model seeks the most discriminative information and uses it to classify industrial components with fine-grained shape attributes. Once a 3D model is identified, one of the category-specific CNNs is tested for pose regression in the second phase. The mixed data for learning object categories is useful in domain adaptation and attention mechanism in our system. We validate our data-driven method with 88 component models, and the experimental results are qualitatively demonstrated. Also, the CNNs trained with various conditions of mixed data are quantitatively analyzed.
Publisher
IEEE Robotics and Automation Society
Issue Date
2017-07
Language
English
Citation

2017 IEEE International Conference on Robotics and Automation, ICRA 2017, pp.2003 - 2010

DOI
10.1109/ICRA.2017.7989232
URI
http://hdl.handle.net/10203/227677
Appears in Collection
EE-Conference Papers(학술회의논문)
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