Data-driven multi-frame 3D motion estimation

We investigate how the temporal evolution of image point correspondences over multiple frames can be exploits for 3D motion and structure estimation. In comparison to other multi-frame approaches we do not separate the establishment of feature point correspondences and the 3D motion parameter computation. As a result our approach does not suffer from the limitations of the traditional two step approaches where even small errors in point correspondence can lead to large errors in 3D motion parameters. Experimental results on synthetic and real video sequences show that the estimation error is reduced when processing more than two frames simultaneously.

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