  We present a novel high-resolution and challenging stereo dataset framing
indoor scenes annotated with dense and accurate ground-truth disparities.
Peculiar to our dataset is the presence of several specular and transparent
surfaces, i.e. the main causes of failures for state-of-the-art stereo
networks. Our acquisition pipeline leverages a novel deep space-time stereo
framework which allows for easy and accurate labeling with sub-pixel precision.
We release a total of 419 samples collected in 64 different scenes and
annotated with dense ground-truth disparities. Each sample include a
high-resolution pair (12 Mpx) as well as an unbalanced pair (Left: 12 Mpx,
Right: 1.1 Mpx). Additionally, we provide manually annotated material
segmentation masks and 15K unlabeled samples. We evaluate state-of-the-art deep
networks based on our dataset, highlighting their limitations in addressing the
open challenges in stereo and drawing hints for future research.
