@conference {Michel2017, title = {Global hypothesis generation for 6D object pose estimation}, booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017}, volume = {2017-Janua}, year = {2017}, month = {dec}, pages = {115{\textendash}124}, abstract = {This paper addresses the task of estimating the 6D pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii) Select and refine a pose from the pool. This work focuses on the second step. While all existing approaches generate the hypotheses pool via local reasoning, e.g. RANSAC or Hough-voting, we are the first to show that global reasoning is beneficial at this stage. In particular, we formulate a novel fully-connected Conditional Random Field (CRF) that outputs a very small number of pose-hypotheses. Despite the potential functions of the CRF being non-Gaussian, we give a new and efficient two-step optimization procedure, with some guarantees for optimality. We utilize our global hypotheses generation procedure to produce results that exceed state-of-the-art for the challenging "Occluded Object Dataset".}, isbn = {9781538604571}, doi = {10.1109/CVPR.2017.20}, url = {http://arxiv.org/abs/1612.02287}, author = {Michel, Frank and Kirillov, Alexander and Brachmann, Eric and Krull, Alexander and Gumhold, Stefan and Savchynskyy, Bogdan and Carsten Rother} }