Publication
Learning 6DoF Object Poses from Synthetic Single Channel Images
Jason Raphael Rambach; Chengbiao Deng; Alain Pagani; Didier Stricker
In: Proceedings of the 17th IEEE ISMAR |. IEEE International Symposium on Mixed and Augmented Reality (ISMAR-2018), 17th, October 16-20, München, Germany, IEEE, 2018.
Abstract
Estimation of 6DoF object poses from single images is a problem
of great interest in augmented reality and robotics research since it
enables interaction with the object or initialization of pose tracking.
Approaches utilizing deep neural networks have shown good performance,
however the majority of them rely on training on real images
of the objects which can be challenging in terms of ground truth pose
acquisition, scalability and full coverage of possible poses. In this
paper, we disregard all depth and color information and train a CNN
to directly regress 6DoF object poses using only synthetic single
channel edge enhanced images. We evaluate our approach against
the state-of-the-art using synthetic training images and show a significant
improvement on the commonly used LINEMOD benchmark
dataset.