D$^{2}$CO: Fast and Robust Registration of 3D Textureless Objects Using the Directional Chamfer Distance

Abstract

This paper introduces a robust and efficient vision based method for object detection and 3D pose estimation that exploits a novel edge-based registration algorithm we called Direct Directional Chamfer Optimization (D$^{2}$CO). Our approach is able to handle textureless and partially occluded objects and does not require any off-line object learning step. Depth edges and visible patterns extracted from the 3D CAD model of the object are matched against edges detected in the current grey level image by means of a 3D distance transform represented by an image tensor, that encodes the minimum distance to an edge point in a joint direction/location space. D$^{2}$CO refines the object position employing a non-linear optimization procedure, where the cost being minimized is extracted directly from the 3D image tensor. Differently from other popular registration algorithms as ICP, that require to constantly update the correspondences between points, our approach does not require any iterative re-association step: the data association is implicitly optimized while inferring the object position. This enables D$^{2}$CO to obtain a considerable gain in speed over other registration algorithms while presenting a wider basin of convergence. We tested our system with a set of challenging untextured objects in presence of occlusions and cluttered background, showing accurate results and often outperforming other state-of-the-art methods.