Order-adaptive and illumination-aware variational optical flow refinement

Abstract

Variational approaches form an inherent part of most state-of-the-art pipeline approaches for optical flow computation. As the final step of the pipeline, the aim is to refine an initial flow field typically obtained by inpainting non-dense matches in order to provide highly accurate results. In this paper, we take advantage of recent improvements in variational optical flow estimation to construct an advanced variational model for this final refinement step. By combining an illumination aware data term with an order adaptive smoothness term, we obtain a highly flexible model that is able to cope well with a broad variety of different scenarios. Moreover, we propose the use of an additional reduced coarse-to-fine scheme instead of an exclusive initialisation scheme, which not only allows to refine the initialisation but also allows to correct larger erroneous displacements. Experiments on recent optical flow benchmarks show the advantages of the advanced variational refinement and the reduced coarse to fine scheme. The proposed order-adaptive method not only allows to significantly improve results compared to pipeline approaches based on traditional first-order refinement techniques, it also allows to outperform recent pure variational methods with full coarse-to-fine schemes.