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In this paper, we propose our Correlation For Completion Network (CFCNet), anend-to-end deep learning model that uses the correlation between two datasources to perform sparse depth completion. CFCNet learns to capture, to thelargest extent, the semantically correlated features between RGB and depthinformation. Through pairs of image pixels and the visible measurements in asparse depth map, CFCNet facilitates feature-level mutual transformation ofdifferent data sources. Such a transformation enables CFCNet to predictfeatures and reconstruct data of missing depth measurements according to theircorresponding, transformed RGB features. We extend canonical correlationanalysis to a 2D domain and formulate it as one of our training objectives(i.e. 2d deep canonical correlation, or "2D2CCA loss"). Extensive experimentsvalidate the ability and flexibility of our CFCNet compared to thestate-of-the-art methods on both indoor and outdoor scenes with differentreal-life sparse patterns. Codes are available at:https://github.com/choyingw/CFCNet.

Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion