  Unsupervised depth completion aims to recover dense depth from the sparse one
without using the ground-truth annotation. Although depth measurement obtained
from LiDAR is usually sparse, it contains valid and real distance information,
i.e., scale-consistent absolute depth values. Meanwhile, scale-agnostic
counterparts seek to estimate relative depth and have achieved impressive
performance. To leverage both the inherent characteristics, we thus suggest to
model scale-consistent depth upon unsupervised scale-agnostic frameworks.
Specifically, we propose the decomposed scale-consistent learning (DSCL)
strategy, which disintegrates the absolute depth into relative depth prediction
and global scale estimation, contributing to individual learning benefits. But
unfortunately, most existing unsupervised scale-agnostic frameworks heavily
suffer from depth holes due to the extremely sparse depth input and weak
supervised signal. To tackle this issue, we introduce the global depth guidance
(GDG) module, which attentively propagates dense depth reference into the
sparse target via novel dense-to-sparse attention. Extensive experiments show
the superiority of our method on outdoor KITTI benchmark, ranking 1st and
outperforming the best KBNet more than 12% in RMSE. In addition, our approach
achieves state-of-the-art performance on indoor NYUv2 dataset.
