{"url":"/dataset/interiornet","name":"InteriorNet","full_name":null,"description_markdown":"**InteriorNet** is a RGB-D for large scale interior scene understanding and mapping. The dataset contains 20M images created by pipeline:\r\n\r\n* (A) the authors collected around 1 million CAD models provided by world-leading furniture manufacturers.\r\n* (B) based on those models, around 1,100 professional designers create around 22 million interior layouts. Most of such layouts have been used in real-world decorations.\r\n* (C) For each layout, authors generate a number of configurations to represent different random lightings and simulation of scene change over time in daily life.\r\n* (D) Authors provide an interactive simulator (ViSim) to help for creating ground truth IMU, events, as well as monocular or stereo camera trajectories including hand-drawn, random walking and neural network based realistic trajectory.\r\n* (E) All supported image sequences and ground truth.\r\n\r\nSource: [InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset](/paper/interiornet-mega-scale-multi-sensor-photo)\r\nImage Source: [InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset](/paper/interiornet-mega-scale-multi-sensor-photo)","description_withheld":null,"homepage":"https://interiornet.org/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/interiornet-mega-scale-multi-sensor-photo","title":"InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset","first_author":"Wenbin Li","url":null},"license":{"name":"CC BY-NC-ND 4.0","url":"https://creativecommons.org/licenses/by-nc-nd/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Simultaneous Localization and Mapping","url":"/task/simultaneous-localization-and-mapping","datasets_with_task":"/datasets/task/simultaneous-localization-and-mapping"}],"languages":[],"variants":["InteriorNet"],"data_loaders":[{"repo":"https://github.com/wangjona9/PredictingHousePrices","url":"https://github.com/wangjona9/PredictingHousePrices","frameworks":["tf","pytorch"]}],"num_papers_in_archive":30,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}