<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michel, Frank</style></author><author><style face="normal" font="default" size="100%">Kirillov, Alexander</style></author><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Krull, Alexander</style></author><author><style face="normal" font="default" size="100%">Gumhold, Stefan</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global hypothesis generation for 6D object pose estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">dec</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1612.02287</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2017-Janua</style></volume><pages><style face="normal" font="default" size="100%">115–124</style></pages><isbn><style face="normal" font="default" size="100%">9781538604571</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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 &quot;Occluded Object Dataset&quot;.</style></abstract></record></records></xml>