Global hypothesis generation for 6D object pose estimation

TitleGlobal hypothesis generation for 6D object pose estimation
Publication TypeConference Paper
Year of Publication2017
AuthorsMichel, F, Kirillov, A, Brachmann, E, Krull, A, Gumhold, S, Savchynskyy, B, Rother, C
Conference NameProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Date Publisheddec
ISBN Number9781538604571

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".

Citation KeyMichel2017