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

Abstract 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 normal and Gaussian curvature, raw image intensity and intensity gradient, raw image contours, and contour orientation and curvature. 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 pose parameters is reduced to a fitting problem between the implicit equation and the observed data points. By considering translation-independent observables such as surface normal and curvature, this process is further decomposed into first determining orientation and then determining translation. Applications to object recognition are described, and an implementation is presented.

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