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

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