  Existing depth completion methods are often targeted at a specific sparse
depth type and generalize poorly across task domains. We present a method to
complete sparse/semi-dense, noisy, and potentially low-resolution depth maps
obtained by various range sensors, including those in modern mobile phones, or
by multi-view reconstruction algorithms. Our method leverages a data-driven
prior in the form of a single image depth prediction network trained on
large-scale datasets, the output of which is used as an input to our model. We
propose an effective training scheme where we simulate various sparsity
patterns in typical task domains. In addition, we design two new benchmarks to
evaluate the generalizability and the robustness of depth completion methods.
Our simple method shows superior cross-domain generalization ability against
state-of-the-art depth completion methods, introducing a practical solution to
high-quality depth capture on a mobile device. The code is available at:
https://github.com/YvanYin/FillDepth.
