  Most existing algorithms for depth estimation from single monocular images
need large quantities of metric groundtruth depths for supervised learning. We
show that relative depth can be an informative cue for metric depth estimation
and can be easily obtained from vast stereo videos. Acquiring metric depths
from stereo videos is sometimes impracticable due to the absence of camera
parameters. In this paper, we propose to improve the performance of metric
depth estimation with relative depths collected from stereo movie videos using
existing stereo matching algorithm. We introduce a new "Relative Depth in
Stereo" (RDIS) dataset densely labelled with relative depths. We first pretrain
a ResNet model on our RDIS dataset. Then we finetune the model on RGB-D
datasets with metric ground-truth depths. During our finetuning, we formulate
depth estimation as a classification task. This re-formulation scheme enables
us to obtain the confidence of a depth prediction in the form of probability
distribution. With this confidence, we propose an information gain loss to make
use of the predictions that are close to ground-truth. We evaluate our approach
on both indoor and outdoor benchmark RGB-D datasets and achieve
state-of-the-art performance.
