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K. Park, J. Prankl, M. Zillich, M. Vincze:
"Pose Estimation of Similar Shape Objects using Convolutional Neural Network trained by Synthetic data";
Vortrag: OAGM & ARW 2017 Joint Workshop on "Vision, Automation and Robotics", Wien; 10.05.2017 - 12.05.2017; in: "Proceedings of the OAGM-ARW Joint Workshop 2017", Verlag der Technischen Universität Graz, (2017), ISBN: 978-3-85125-524-9; 5 S.

The objective of this paper is accurate 6D pose
estimation from 2.5D point clouds for object classes with a
high shape variation, such as vegetables and fruit. General
pose estimation methods usually focus on calculating rigid
transformations between known models and the target scene,
and do not explicitly consider shape variations. We employ
deep convolutional neural networks (CNN), which show robust
and state of the art performance for the 2D image domain.
In contrast, normally the performance of pose estimation from
point clouds is weak, because it is hard to prepare large enough
annotated training data. To overcome this issue, we propose an
autonomous generation process of synthetic 2.5D point clouds
covering different shape variations of the objects. The synthetic
data is used to train the deep CNN model in order to estimate
the object poses. We propose a novel loss function to guide
the estimator to have larger feature distances for different
poses, and to directly estimate the correct object pose. We
performed an evaluation using real objects, where the training
was conducted with artificial CAD models downloaded from a
public web resource. The results indicate that our approach is
suitable for real world robotic applications.