This paper proposes a sparse depth completion approach that is uncertainty-aware. We propose a novel paradigm of learning the uncertainty of the input and propagate it through the network until the final output. Our approach can be used with other sparse problems such as sparse optical flow and time-of-flight rectification.
Generally, convolutional neural networks (CNNs) process data on a regular grid, e.g. data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open research problem with numerous applications in …