Hough voting for 3D object recognition under occlusion and clutter

This work proposes a novel approach for the detection of free-form shapes in a 3D space. The proposed method matches 3D features through their descriptions to attain correspondences, then accumulates evidence of the presence of the object(s) being sought by verifying the consensus of correspondences within a 3D Hough space. Our approach is capable of recognizing 3D shapes under significant degree of occlusion and clutter and can deal with multiple instances of the shape to be recognized. We validate our proposal by means of a quantitative experimental comparison to the state of the art over two datasets acquired with different sensors (a laser scanner and a stereo camera) and characterized by high degrees of clutter and occlusion. In addition, we propose an extension of the approach to RGB-D (i.e., color and depth) data together with results concerning 3D object recognition from RGB-D data acquired by a Microsoft Kinect sensor.