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Datasets have gained an enormous amount of popularity in the computer visioncommunity, from training and evaluation of Deep Learning-based methods tobenchmarking Simultaneous Localization and Mapping (SLAM). Without a doubt,synthetic imagery bears a vast potential due to scalability in terms of amountsof data obtainable without tedious manual ground truth annotations ormeasurements. Here, we present a dataset with the aim of providing a higherdegree of photo-realism, larger scale, more variability as well as serving awider range of purposes compared to existing datasets. Our dataset leveragesthe availability of millions of professional interior designs and millions ofproduction-level furniture and object assets -- all coming with fine geometricdetails and high-resolution texture. We render high-resolution and highframe-rate video sequences following realistic trajectories while supportingvarious camera types as well as providing inertial measurements. Together withthe release of the dataset, we will make executable program of our interactivesimulator software as well as our renderer available athttps://interiornetdataset.github.io. To showcase the usability and uniquenessof our dataset, we show benchmarking results of both sparse and dense SLAMalgorithms.

InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset