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.

翻译：深度补全的主要功能是补偿硬件传感器稀疏深度测量数量不足且不可预测的问题。然而，现有关于深度补全的研究假设稀疏度——即点云数量或激光雷达线数——在训练和测试中固定不变。因此，当稀疏深度数量发生显著变化时，补全性能会严重下降。为了解决这一问题，我们提出了稀疏自适应深度精化（SDR）框架，该框架利用稀疏深度点对单目深度估计进行精化。针对SDR，我们提出了掩膜空间传播网络（MSPN），通过在整个深度图中逐步传播稀疏深度信息，有效处理不同数量稀疏深度的SDR任务。实验结果表明，MSPN在SDR和传统深度补全场景下均达到了最先进的性能。