InteriorNet：大規模マルチセンサー実写風屋内シーンデータセット
InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
数百万のプロのインテリアデザインと家具資産を活用し、高解像度・高フレームレートの実写風屋内動画と慣性計測を提供する大規模データセットを構築し、SLAMアルゴリズムのベンチマークを示した。
著者: Wenbin Li, Sajad Saeedi, John McCormac, Ronald Clark, Dimos Tzoumanikas, Qing Ye, Yuzhong Huang, Rui Tang, Stefan Leutenegger
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
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.
