  Depth completion has attracted extensive attention recently due to the
development of autonomous driving, which aims to recover dense depth map from
sparse depth measurements. Convolutional spatial propagation network (CSPN) is
one of the state-of-the-art methods in this task, which adopt a linear
propagation model to refine coarse depth maps with local context. However, the
propagation of each pixel occurs in a fixed receptive field. This may not be
the optimal for refinement since different pixel needs different local context.
To tackle this issue, in this paper, we propose a deformable spatial
propagation network (DSPN) to adaptively generates different receptive field
and affinity matrix for each pixel. It allows the network obtain information
with much fewer but more relevant pixels for propagation. Experimental results
on KITTI depth completion benchmark demonstrate that our proposed method
achieves the state-of-the-art performance.
