%0 Generic %1 mehl2022replication %A Mehl, Lukas %A Beschle, Cedric %A Barth, Andrea %A Bruhn, Andrés %D 2022 %K %R 10.18419/darus-2890 %T Replication Data for: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation %X Results of our proposed optical flow method on the Sintel and KITTI datasets. We provide the benchmark results before and after applying our refinement approach.Additionally, we provide a supplementary material to our paper with more details on the minimisation, the numerical solution and additional results.The data is structured as follows:The file supplement.pdf contains the supplementary material of the paper.Additionally, there are 10 data folders according to the test results in Table 3 of our paper. They are named using the scheme input/output_datasetName_methodName:input/output: whether the files are the input for our method or the output of our methoddatasetName: one of kitti, sintelClean or sintelFinal relating to either the KITTI dataset Menze, CVPR 2015 or the Sintel dataset Butler, ECCV 2012methodName: one of raft or raftWarm Teed, ECCV 2020 or the refined variants from our method.The data can be used to build on the output of our method or to compare a novel approach against our method by using the same input data.The dataformat is pdf for the supplementary material and png and flo for the optical flow files as used in the respective benchmarks.

@misc{mehl2022replication, abstract = {Results of our proposed optical flow method on the Sintel and KITTI datasets. We provide the benchmark results before and after applying our refinement approach.Additionally, we provide a supplementary material to our paper with more details on the minimisation, the numerical solution and additional results.The data is structured as follows:The file supplement.pdf contains the supplementary material of the paper.Additionally, there are 10 data folders according to the test results in Table 3 of our paper. They are named using the scheme {input/output}_{datasetName}_{methodName}:input/output: whether the files are the input for our method or the output of our methoddatasetName: one of kitti, sintelClean or sintelFinal relating to either the KITTI dataset [Menze, CVPR 2015] or the Sintel dataset [Butler, ECCV 2012]methodName: one of raft or raftWarm [Teed, ECCV 2020] or the refined variants from our method.The data can be used to build on the output of our method or to compare a novel approach against our method by using the same input data.The dataformat is pdf for the supplementary material and png and flo for the optical flow files as used in the respective benchmarks. }, added-at = {2023-08-31T15:02:31.000+0200}, affiliation = {Mehl, Lukas/Institute for Visualization and Interactive Systems, University of Stuttgart, Beschle, Cedric/Institute for Applied Analysis and Numerical Simulation, University of Stuttgart, Barth, Andrea/Institute for Applied Analysis and Numerical Simulation, University of Stuttgart, Bruhn, Andrés/Institute for Visualization and Interactive Systems, University of Stuttgart}, author = {Mehl, Lukas and Beschle, Cedric and Barth, Andrea and Bruhn, Andrés}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/28c53db827fcd53375b646c862f34d6dc/puma-wartung}, doi = {10.18419/darus-2890}, howpublished = {Dataset}, interhash = {5c7ab3fc59a4466729e5a798f56e5834}, intrahash = {8c53db827fcd53375b646c862f34d6dc}, keywords = {}, note = {Related to: L. Mehl, C. Beschle, A. Barth, A. Bruhn: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation. Proc. International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). Lecture Notes in Computer Science, Vol. 10302, 140-152, Springer, 2021. doi: 10.1007/978-3-030-75549-2_12}, orcid-numbers = {Barth, Andrea/0000-0003-1642-3625, Bruhn, Andrés/0000-0003-0423-7411}, timestamp = {2023-08-31T13:02:31.000+0200}, title = {Replication Data for: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation}, year = 2022 }