Rigid 3-D motion estimation using neural networks and initially estimated 2-D motion data

This paper extends a known efficient technique for rigid three-dimensional (3-D) motion estimation so as to make it applicable to motion-estimation pr oblems occuring in image sequence coding applications. The known technique estimates 3-D motion using previously evaluated 3-D correspondence. However , in image sequence coding applications, 3-D correspondence is unknown and usually only two-dimensional (2-D) motion vectors are initially available. The novel neural network (NN) introduced in this paper uses initially estim ated 2-D motion vectors to estimate 3-D rigid motion, and is therefore suit able for image sequence coding applications. Moreover, it is shown that the NN introduced in this paper performs extremely well even in cases where 3- D correspondence is known with accuracy. Experimental results are presented for the evaluation of the proposed scheme.