  State-of-the-art techniques proposed for 6D object pose recovery depend on
occlusion-free point clouds to accurately register objects in 3D space. To
reduce this dependency, we introduce a novel architecture called Iterative
Hough Forest with Histogram of Control Points that is capable of estimating
occluded and cluttered objects' 6D pose given a candidate 2D bounding box. Our
Iterative Hough Forest is learnt using patches extracted only from the positive
samples. These patches 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 this scale-variance is leveraged during inference,
where the initial pose estimation of the object is iteratively refined based on
more discriminative control points by using our Iterative Hough Forest. We
conduct experiments on several test objects of a publicly available dataset to
test our architecture and to compare with the state-of-the-art.
