Unsupervised depth completion aims to recover dense from the sparse one without using ground-truth annotation. Although measurement obtained LiDAR is usually sparse, it contains valid and real distance information, i.e., scale-consistent absolute values. Meanwhile, scale-agnostic counterparts seek estimate relative have achieved impressive performance. To leverage both inherent characteristics,...