Robust 3D feature detection using dictionary-based relaxation
Nigel G. Sharp, Edwin R. Hancock · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
The problem of grouping 3D coplanar line segmented obtained from a single view is addressed. The proposed method is efficient and has been tested on both synthetic and real images. First, a Hough-based algorithm is used to detect 2D line segments in a sequence of images representing a 3D scene. Secondly, the 3D coordinates of the line segments are estimated, at each time instant, by means of an extended Kalman filter, based on the displacements (u,v) of the line segment endpoints on the image plane. Finally, 3D coplanar segments are grouped by a 3D voting approach. The novelty of this method lies in the possibility of using a simple voting scheme similar to that associated with the standard Hough transform for line extraction, where each edge point votes for a sheaf of rectilinear lines. In the proposed approach, each line segment votes for a sheaf of planes.