  We introduce a novel framework for training deep stereo networks effortlessly
and without any ground-truth. By leveraging state-of-the-art neural rendering
solutions, we generate stereo training data from image sequences collected with
a single handheld camera. On top of them, a NeRF-supervised training procedure
is carried out, from which we exploit rendered stereo triplets to compensate
for occlusions and depth maps as proxy labels. This results in stereo networks
capable of predicting sharp and detailed disparity maps. Experimental results
show that models trained under this regime yield a 30-40% improvement over
existing self-supervised methods on the challenging Middlebury dataset, filling
the gap to supervised models and, most times, outperforming them at zero-shot
generalization.
