We propose a novel approach for optical flow estima tion, targeted at large displacements with significant oc clusions. It consists of two steps: i) dense matching by edge-preserving interpolation from a sparse set of matches; ii) variational energy minimization initialized with the dense matches. The sparse-to-dense interpolation relies on an appropriate choice of the distance, namely an edge aware geodesic distance. This distance is tailored to han dle occlusions and motion boundaries -two common and difficult issues for optical flow computation. We also pro pose an approximation scheme for the geodesic distance to allow fast computation without loss of performance. Sub sequent to the dense interpolation step, standard one-level variational energy minimization is carried out on the dense matches to obtain the final flow estimation. The proposed approach, called Edge-Preserving Interpolation of Corre spondences (EpicFlow) is fast and robust to large displace ments. It significantly outperforms the state of the art on MPI-Sintel and performs on par on Kitti and Middlebury.
Regrading sparse-to-dense interpolation, EpicFlow REF is a seminal work.
EpicFlow: Edge-preserving interpolation of correspondences for optical flow
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science