  In this paper we consider the problem of estimating a dense depth map from a
set of sparse LiDAR points. We use techniques from compressed sensing and the
recently developed Alternating Direction Neural Networks (ADNNs) to create a
deep recurrent auto-encoder for this task. Our architecture internally performs
an algorithm for extracting multi-level convolutional sparse codes from the
input which are then used to make a prediction. Our results demonstrate that
with only two layers and 1800 parameters we are able to out perform all
previously published results, including deep networks with orders of magnitude
more parameters.
