  The main function of depth completion is to compensate for an insufficient
and unpredictable number of sparse depth measurements of hardware sensors.
However, existing research on depth completion assumes that the sparsity -- the
number of points or LiDAR lines -- is fixed for training and testing. Hence,
the completion performance drops severely when the number of sparse depths
changes significantly. To address this issue, we propose the sparsity-adaptive
depth refinement (SDR) framework, which refines monocular depth estimates using
sparse depth points. For SDR, we propose the masked spatial propagation network
(MSPN) to perform SDR with a varying number of sparse depths effectively by
gradually propagating sparse depth information throughout the entire depth map.
Experimental results demonstrate that MPSN achieves state-of-the-art
performance on both SDR and conventional depth completion scenarios.
