  In this paper, we introduce a novel training method for making any monocular
depth network learn absolute scale and estimate metric road-scene depth just
from regular training data, i.e., driving videos. We refer to this training
framework as StableCamH. The key idea is to leverage cars found on the road as
sources of scale supervision but to incorporate them in the training robustly.
StableCamH detects and estimates the sizes of cars in the frame and aggregates
scale information extracted from them into a camera height estimate whose
consistency across the entire video sequence is enforced as scale supervision.
This realizes robust unsupervised training of any, otherwise scale-oblivious,
monocular depth network to become not only scale-aware but also metric-accurate
without the need for auxiliary sensors and extra supervision. Extensive
experiments on the KITTI and Cityscapes datasets show the effectiveness of
StableCamH and its state-of-the-art accuracy compared with related methods. We
also show that StableCamH enables training on mixed datasets of different
camera heights, which leads to larger-scale training and thus higher
generalization. Metric depth reconstruction is essential in any road-scene
visual modeling, and StableCamH democratizes its deployment by establishing the
means to train any model as a metric depth estimator.
