  In this paper, we adapt the geodesic distance-based recursive filter to the
sparse data interpolation problem. The proposed technique is general and can be
easily applied to any kind of sparse data. We demonstrate the superiority over
other interpolation techniques in three experiments for qualitative and
quantitative evaluation.
  In addition, we compare our method with the popular interpolation algorithm
presented in the EpicFlow optical flow paper that is intuitively motivated by a
similar geodesic distance principle. The comparison shows that our algorithm is
more accurate and considerably faster than the EpicFlow interpolation
technique.
