  Interpolation of sparse pixel information towards a dense target resolution
finds its application across multiple disciplines in computer vision.
State-of-the-art interpolation of motion fields applies model-based
interpolation that makes use of edge information extracted from the target
image. For depth completion, data-driven learning approaches are widespread.
Our work is inspired by latest trends in depth completion that tackle the
problem of dense guidance for sparse information. We extend these ideas and
create a generic cross-domain architecture that can be applied for a multitude
of interpolation problems like optical flow, scene flow, or depth completion.
In our experiments, we show that our proposed concept of Sparse Spatial Guided
Propagation (SSGP) achieves improvements to robustness, accuracy, or speed
compared to specialized algorithms.
