An efficient method for tracking the attitude and translation of an object from a sequence of dense range images is demonstrated. The range data are sine coded and then Fourier transformed. By this process, planar surfaces in the image produce distinctive peaks in the Fourier transform spectrum. The position of a peak provides a direct measure of the normal of the plane. Tracking the positions of these peaks enables tracking the orientation of a known object. After computing and undoing the rotation, the translation is obtained by measuring the 3-D centroid of the segmented planar surfaces. All computations are closed form. The Fourier transform method is also used for segmentation. The integrative nature of the Fourier transform is very effective in attenuating the effect of noise and outliers. Results of tracking an object in a simulated range image sequence show high accuracy [0.2 deg in orientation and 0.2% root-mean-square (rms) error in translation with respect to the width of the object]. The system runs presently at 3 to 5 frames/s. The method promises real-time (video rate) performance with the addition of accelerator hardware for computing the Fourier transform. The approach is well suited for dynamic robotic vision applications.