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Non-local Total Generalized Variation for Optical Flow Estimation*
René Ranftl1, Kristian Bredies2, and Thomas Pock1, 3
1Institute for Computer Graphics and Vision, Graz University of Technology, Austria
2Institute for Mathematics and Scientific Computing, University of Graz, Austria
3Safety & Security Department, AIT Austrian Institute of Technology, Austria
Abstract. In this paper we introduce a novel higher-order regularization term. The proposed regularizer is a non-local extension of the popular second-order Total Generalized variation, which favors piecewise affine solutions and allows to incorporate soft-segmentation cues into the regularization term. These properties make this regularizer especially appealing for optical flow estimation, where it offers accurately localized motion boundaries and allows to resolve ambiguities in the matching term. We additionally propose a novel matching term which is robust to illumination and scale changes, two major sources of errors in optical flow estimation algorithms. We extensively evaluate the proposed regularizer and data term on two challenging benchmarks, where we are able to obtain state of the art results. Our method is currently ranked first among classical two-frame optical flow methods on the KITTI optical flow benchmark.
*René Ranftl and Thomas Pock acknowledge support from the Austrian Science Fund (FWF) under the projects No. I1148 and Y729. Kristian Bredies acknowledges support by the Austrian Science Fund special research grant SFB F32 “Mathematical Optimization and Applications in Biomedical Sciences”.
LNCS 8689, p. 439 ff.
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© Springer International Publishing Switzerland 2014