RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints

CVPR 2018 Asako KanezakiYasuyuki MatsushitaYoshifumi Nishida

We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known viewpoint labels for training, our method treats the viewpoint labels as latent variables, which are learned in an unsupervised manner during the training using an unaligned object dataset... (read more)

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