Andreas Wedel, Thomas Brox, Tobi Vaudrey, Clemens Rabe, Uwe Franke, Daniel CremersAbstractBuilding upon recent developments in
optical flow and stereo matching estimation, we propose a variational
framework for the estimation of stereoscopic scene flow, i.e., the
motion of points in the three-dimensional world from stereo image
sequences. The proposed algorithm takes into account image pairs from
two consecutive times and computes both depth and a 3D motion vector
associated with each point in the image. In contrast to previous works,
we partially decouple the depth estimation from the motion estimation,
which has many practical advantages. The variational formulation is
quite flexible and can handle both sparse or dense disparity maps. The
proposed method is very efficient; with the depth map being computed on
an FPGA, and the scene flow computed on the GPU, the proposed algorithm
runs at frame rates of 20 frames per second on QVGA images (320×240
pixels). Furthermore, we present solutions to two important problems in
scene flow estimation: violations of intensity consistency between input
images, and the uncertainty measures for the scene flow result. [Download] [View] [BibTeX] |
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