  LiDAR depth maps provide environmental guidance in a variety of applications.
However, such depth maps are typically sparse and insufficient for complex
tasks such as autonomous navigation. State of the art methods use image guided
neural networks for dense depth completion. We develop a guided convolutional
neural network focusing on gathering dense and valid information from sparse
depth maps. To this end, we introduce a novel layer with spatially variant and
content-depended dilation to include additional data from sparse input.
Furthermore, we propose a sparsity invariant residual bottleneck block. We
evaluate our Dense Validity Mask Network (DVMN) on the KITTI depth completion
benchmark and achieve state of the art results. At the time of submission, our
network is the leading method using sparsity invariant convolution.
