Global Hypothesis Generation for 6D Object Pose Estimation
Frank Michel, Alexander Kirillov, Eric Brachmann, Alexander Krull, Stefan Gumhold, Bogdan Savchynskyy, Carsten Rother
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, 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".
BibTeX
@inproceedings{cvpr2017_globalhypothesis,
title = {Global Hypothesis Generation for 6D Object Pose Estimation},
author = {Frank Michel and Alexander Kirillov and Eric Brachmann and Alexander Krull and Stefan Gumhold and Bogdan Savchynskyy and Carsten Rother},
booktitle = {CVPR 2017},
year = {2017}
}