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

DVMN: Dense Validity Mask Network for Depth Completion