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

SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation