Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion
This addresses the problem of incomplete depth maps for applications like robotics and autonomous driving, representing an incremental improvement over existing methods.
The paper tackles sparse depth completion by proposing CFCNet, an end-to-end deep learning model that uses correlation between RGB and depth data to predict missing depth measurements, achieving state-of-the-art performance on indoor and outdoor scenes with various sparse patterns.
In this paper, we propose our Correlation For Completion Network (CFCNet), an end-to-end deep learning model that uses the correlation between two data sources to perform sparse depth completion. CFCNet learns to capture, to the largest extent, the semantically correlated features between RGB and depth information. Through pairs of image pixels and the visible measurements in a sparse depth map, CFCNet facilitates feature-level mutual transformation of different data sources. Such a transformation enables CFCNet to predict features and reconstruct data of missing depth measurements according to their corresponding, transformed RGB features. We extend canonical correlation analysis to a 2D domain and formulate it as one of our training objectives (i.e. 2d deep canonical correlation, or "2D2CCA loss"). Extensive experiments validate the ability and flexibility of our CFCNet compared to the state-of-the-art methods on both indoor and outdoor scenes with different real-life sparse patterns. Codes are available at: https://github.com/choyingw/CFCNet.