A learning-based variable size part extraction architecture for 6D object pose recovery in depth images

Caner Sahin, Rigas Kouskouridas, Tae-Kyun Kim

Abstract: Highlights•We propose part-based Iterative Hough Forests (IHF) for 6 DoF object registration.•The parts are represented with scale-variant Histogram of Control Points features.•Scale-variability of the features is iteratively exploited for pose refinement.•The refinement is improved by an automatic variable size part extraction framework.