Model building of three-dimensional polyhedral objects using 3D edge information and hemispheric histogram

An algorithm for extracting edges and plane regions of a polyhedral object in a three-dimensional (3D) range image is described. The object may be Convex or nonconvex. A model of the object is built with the regions extracted. Possible extension to cylindrical objects is also considered. The range images are obtained with a novel range-finder camera that can produce 128 × 256 or 256 × 256 surface element (surfcels) images. The edge detection is accomplished in five steps and yields edges one surfcel wide. The region-finding algorithm relies on the concept of the "hemispheric histogram." The histogram is built with the normals of groups of surfcels (patches) forming the image. Analysis of the hemispheric histogram gives global information on the surface orientation of the visible regions of an object. Once these regions are extracted, they are expanded with a region growing process. Geometric properties of the regions are computed by a simple contour following algorithm. Then, a relational model of the regions is built. The model gathers information that is independent of the position and orientation of the object ill the reference plane and could be Used for object recognition in an unsupervised 3D vision system.