  Deep-learning metrics have recently demonstrated extremely good performance
to match image patches for stereo reconstruction. However, training such
metrics requires large amount of labeled stereo images, which can be difficult
or costly to collect for certain applications. The main contribution of our
work is a new semi-supervised method for learning deep metrics from unlabeled
stereo images, given coarse information about the scenes and the optical
system. Our method alternatively optimizes the metric with a standard
stochastic gradient descent, and applies stereo constraints to regularize its
prediction. Experiments on reference data-sets show that, for a given network
architecture, training with this new method without ground-truth produces a
metric with performance as good as state-of-the-art baselines trained with the
said ground-truth. This work has three practical implications. Firstly, it
helps to overcome limitations of training sets, in particular noisy ground
truth. Secondly it allows to use much more training data during learning.
Thirdly, it allows to tune deep metric for a particular stereo system, even if
ground truth is not available.
