  State-of-the-art techniques for 6D object pose recovery depend on
occlusion-free point clouds to accurately register objects in 3D space. To deal
with this shortcoming, we introduce a novel architecture called Iterative Hough
Forest with Histogram of Control Points that is capable of estimating the 6D
pose of occluded and cluttered objects given a candidate 2D bounding box. Our
Iterative Hough Forest (IHF) is learnt using parts extracted only from the
positive samples. These parts are represented with Histogram of Control Points
(HoCP), a "scale-variant" implicit volumetric description, which we derive from
recently introduced Implicit B-Splines (IBS). The rich discriminative
information provided by the scale-variant HoCP features is leveraged during
inference. An automatic variable size part extraction framework iteratively
refines the object's initial pose that is roughly aligned due to the extraction
of coarsest parts, the ones occupying the largest area in image pixels. The
iterative refinement is accomplished based on finer (smaller) parts that are
represented with more discriminative control point descriptors by using our
Iterative Hough Forest. Experiments conducted on a publicly available dataset
report that our approach show better registration performance than the
state-of-the-art methods.
