On using CAD models to compute the pose of curved 3D objects

A new approach is presented for explicitly relating image observables to models of curved three-dimensional objects. This relationship is used for object recognition and positioning. Object models consist of collections of parametric surface patches. The image observables considered are raw range data, surface normals and Gaussian curvature, raw image contours, contour orientation and curvature, raw image intensity, and intensity gradient. Elimination theory provides a method for constructing an implicit equation that relates these observables to the three-dimensional position and orientation of object models. Determining the unknown parameters is reduced to a fitting problem between the implicit equation and served data points. A preliminary implementation is presented