  Estimating scene geometry from data obtained with cost-effective sensors is
key for robots and self-driving cars. In this paper, we study the problem of
predicting dense depth from a single RGB image (monodepth) with optional sparse
measurements from low-cost active depth sensors. We introduce Sparse Auxiliary
Networks (SANs), a new module enabling monodepth networks to perform both the
tasks of depth prediction and completion, depending on whether only RGB images
or also sparse point clouds are available at inference time. First, we decouple
the image and depth map encoding stages using sparse convolutions to process
only the valid depth map pixels. Second, we inject this information, when
available, into the skip connections of the depth prediction network,
augmenting its features. Through extensive experimental analysis on one indoor
(NYUv2) and two outdoor (KITTI and DDAD) benchmarks, we demonstrate that our
proposed SAN architecture is able to simultaneously learn both tasks, while
achieving a new state of the art in depth prediction by a significant margin.
