@conference {Krull2017,
	title = {PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {dec},
	pages = {2566{\textendash}2574},
	abstract = {State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational resources to promising candidates, however, such decisions are non-differentiable. As a result, these algorithms are hard to train in an end-to-end fashion. In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation. Our system optimizes the parameters of an existing state-of-the art pose estimation system using reinforcement learning, where the pose estimation system now becomes the stochastic policy, parametrized by a CNN. Additionally, we present an efficient training algorithm that dramatically reduces computation time. We show empirically that our learned pose estimation procedure makes better use of limited resources and improves upon the state-of-the-art on a challenging dataset. Our approach enables differentiable end-to-end training of complex algorithmic pipelines and learns to make optimal use of a given computational budget.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.275},
	url = {http://arxiv.org/abs/1612.03779},
	author = {Krull, Alexander and Brachmann, Eric and Nowozin, Sebastian and Michel, Frank and Shotton, Jamie and Carsten Rother}
}
@conference {Michel2015,
	title = {Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression},
	year = {2015},
	pages = {181.1{\textendash}181.11},
	abstract = {In this paper, we address the problem of one shot pose estimation of articulated ob-jects from an RGB-D image. In particular, we consider object instances with the topol-ogy of a kinematic chain, i.e. assemblies of rigid parts connected by prismatic or revolute joints. This object type occurs often in daily live, for instance in the form of furniture or electronic devices. Instead of treating each object part separately we are using the rela-tionship between parts of the kinematic chain and propose a new minimal pose sampling approach. This enables us to create a pose hypothesis for a kinematic chain consist-ing of K parts by sampling K 3D-3D point correspondences. To asses the quality of our method, we gathered a large dataset containing four objects and 7000+ annotated RGB-D frames 1 . On this dataset we achieve considerably better results than a modified state-of-the-art pose estimation system for rigid objects.},
	doi = {10.5244/c.29.181},
	author = {Michel, Frank and Krull, Alexander and Brachmann, Eric and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
