  Depth estimation from a single image is an active research topic in computer
vision. The most accurate approaches are based on fully supervised learning
models, which rely on a large amount of dense and high-resolution (HR)
ground-truth depth maps. However, in practice, color images are usually
captured with much higher resolution than depth maps, leading to the
resolution-mismatched effect. In this paper, we propose a novel
weakly-supervised framework to train a monocular depth estimation network to
generate HR depth maps with resolution-mismatched supervision, i.e., the inputs
are HR color images and the ground-truth are low-resolution (LR) depth maps.
The proposed weakly supervised framework is composed of a sharing weight
monocular depth estimation network and a depth reconstruction network for
distillation. Specifically, for the monocular depth estimation network the
input color image is first downsampled to obtain its LR version with the same
resolution as the ground-truth depth. Then, both HR and LR color images are fed
into the proposed monocular depth estimation network to obtain the
corresponding estimated depth maps. We introduce three losses to train the
network: 1) reconstruction loss between the estimated LR depth and the
ground-truth LR depth; 2) reconstruction loss between the downsampled estimated
HR depth and the ground-truth LR depth; 3) consistency loss between the
estimated LR depth and the downsampled estimated HR depth. In addition, we
design a depth reconstruction network from depth to depth. Through distillation
loss, features between two networks maintain the structural consistency in
affinity space, and finally improving the estimation network performance.
Experimental results demonstrate that our method achieves superior performance
than unsupervised and semi-supervised learning based schemes, and is
competitive or even better compared to supervised ones.
