  In this paper, we tackle the problem of depth completion from RGBD data.
Towards this goal, we design a simple yet effective neural network block that
learns to extract joint 2D and 3D features. Specifically, the block consists of
two domain-specific sub-networks that apply 2D convolution on image pixels and
continuous convolution on 3D points, with their output features fused in image
space. We build the depth completion network simply by stacking the proposed
block, which has the advantage of learning hierarchical representations that
are fully fused between 2D and 3D spaces at multiple levels. We demonstrate the
effectiveness of our approach on the challenging KITTI depth completion
benchmark and show that our approach outperforms the state-of-the-art.
