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 …