Iterative Hough Forest with Histogram of Control Points for 6 DoF Object Registration from Depth Images
This addresses the challenge of robust object registration in 3D computer vision for robotics and AR/VR applications, representing an incremental improvement over existing methods.
The paper tackles the problem of 6D object pose estimation from depth images under occlusion and clutter, introducing an Iterative Hough Forest with Histogram of Control Points that achieves state-of-the-art performance on a public dataset.
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.