Datasets have gained an enormous amount of popularity in the computer vision com-
munity, 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 variabil-
ity 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 se-
quences 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 avail-
able 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.