Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images

CVPR 2018 Mahdi RadMarkus OberwegerVincent Lepetit

We propose a simple and efficient method for exploiting synthetic images when training a Deep Network to predict a 3D pose from an image. The ability of using synthetic images for training a Deep Network is extremely valuable as it is easy to create a virtually infinite training set made of such images, while capturing and annotating real images can be very cumbersome... (read more)

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