  Indoor robotics localization, navigation, and interaction heavily rely on
scene understanding and reconstruction. Compared to the monocular vision which
usually does not explicitly introduce any geometrical constraint, stereo
vision-based schemes are more promising and robust to produce accurate
geometrical information, such as surface normal and depth/disparity. Besides,
deep learning models trained with large-scale datasets have shown their
superior performance in many stereo vision tasks. However, existing stereo
datasets rarely contain the high-quality surface normal and disparity ground
truth, which hardly satisfies the demand of training a prospective deep model
for indoor scenes. To this end, we introduce a large-scale synthetic but
naturalistic indoor robotics stereo (IRS) dataset with over 100K stereo RGB
images and high-quality surface normal and disparity maps. Leveraging the
advanced rendering techniques of our customized rendering engine, the dataset
is considerably close to the real-world captured images and covers several
visual effects, such as brightness changes, light reflection/transmission, lens
flare, vivid shadow, etc. We compare the data distribution of IRS with existing
stereo datasets to illustrate the typical visual attributes of indoor scenes.
Besides, we present DTN-Net, a two-stage deep model for surface normal
estimation. Extensive experiments show the advantages and effectiveness of IRS
in training deep models for disparity estimation, and DTN-Net provides
state-of-the-art results for normal estimation compared to existing methods.
