Masked Spatial Propagation Network for Sparsity-Adaptive Depth Refinement
Authors: Jinyoung Jun, Jae-Han Lee, Chang-Su Kim

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.