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

A Learning-based Variable Size Part Extraction Architecture for 6D Object Pose Recovery in Depth