  Datasets have gained an enormous amount of popularity in the computer vision
community, from training and evaluation of Deep Learning-based methods to
benchmarking Simultaneous Localization and Mapping (SLAM). Without a doubt,
synthetic imagery bears a vast potential due to scalability in terms of amounts
of data obtainable without tedious manual ground truth annotations or
measurements. Here, we present a dataset with the aim of providing a higher
degree of photo-realism, larger scale, more variability as well as serving a
wider range of purposes compared to existing datasets. Our dataset leverages
the availability of millions of professional interior designs and millions of
production-level furniture and object assets -- all coming with fine geometric
details and high-resolution texture. We render high-resolution and high
frame-rate video sequences following realistic trajectories while supporting
various camera types as well as providing inertial measurements. Together with
the release of the dataset, we will make executable program of our interactive
simulator software as well as our renderer available at
https://interiornetdataset.github.io. To showcase the usability and uniqueness
of our dataset, we show benchmarking results of both sparse and dense SLAM
algorithms.
