  In this work we investigate the problem of uncertainty estimation for
image-guided depth completion. We extend Deep Basis Fitting (DBF) for depth
completion within a Bayesian evidence framework to provide calibrated per-pixel
variance. The DBF approach frames the depth completion problem in terms of a
network that produces a set of low-dimensional depth bases and a differentiable
least squares fitting module that computes the basis weights using the sparse
depths. By adopting a Bayesian treatment, our Bayesian Deep Basis Fitting
(BDBF) approach is able to 1) predict high-quality uncertainty estimates and 2)
enable depth completion with few or no sparse measurements. We conduct
controlled experiments to compare BDBF against commonly used techniques for
uncertainty estimation under various scenarios. Results show that our method
produces better uncertainty estimates with accurate depth prediction.
