  We propose a novel algorithm for monocular depth estimation that decomposes a
metric depth map into a normalized depth map and scale features. The proposed
network is composed of a shared encoder and three decoders, called G-Net,
N-Net, and M-Net, which estimate gradient maps, a normalized depth map, and a
metric depth map, respectively. M-Net learns to estimate metric depths more
accurately using relative depth features extracted by G-Net and N-Net. The
proposed algorithm has the advantage that it can use datasets without metric
depth labels to improve the performance of metric depth estimation.
Experimental results on various datasets demonstrate that the proposed
algorithm not only provides competitive performance to state-of-the-art
algorithms but also yields acceptable results even when only a small amount of
metric depth data is available for its training.
