{"url":"/dataset/futurehouse","name":"FutureHouse","full_name":null,"description_markdown":"We present a new large-scale photorealistic panoramic dataset named FutureHouse, which has the following characteristics. \r\n     1) It contains over 70,000 high-quality models with high-resolution meshes and physical material. All models are measured in real world standards. \r\n     2) Selected scene layouts are carefully designed by over 100 excellent artists. All of selected layouts are used in realworld display. \r\n     3) It contains 28,579 good panoramic views from 1,752 house-scale scenes. Therefore, it can be used for perspective image tasks as well as omnidirectional image tasks. \r\n     4) More physical material representation. Most materials are represent by microfacet BRDF modeling metalness, and the rest are represent by special shading models, e.g., cloth material and transmission material. \r\n     5) High rendering quality. Benefiting from commercial rendering engine, Unreal engine 4, and powerful deep learning super sampling (DLSS), our renderings have less noise. \r\nOur SVBRDF representation including base color and metalness is capable of producing nonmonochrome specular reflectance.","description_withheld":null,"homepage":"http://yodlee.top/PhyIR/","introduced_date":"2022-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/phyir-physics-based-inverse-rendering-for","title":"PhyIR: Physics-Based Inverse Rendering for Panoramic Indoor Images","first_author":"Zhen Li","url":null},"license":{"name":"GNU 2.0","url":"https://github.com/LZleejean/FutureHouse/blob/main/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Depth Estimation","url":"/task/depth-estimation","datasets_with_task":"/datasets/task/depth-estimation"},{"name":"Surface Normal Estimation","url":"/task/surface-normal-estimation","datasets_with_task":"/datasets/task/surface-normal-estimation"},{"name":"Intrinsic Image Decomposition","url":"/task/intrinsic-image-decomposition","datasets_with_task":"/datasets/task/intrinsic-image-decomposition"},{"name":"Neural Rendering","url":"/task/neural-rendering","datasets_with_task":"/datasets/task/neural-rendering"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["FutureHouse"],"data_loaders":[{"repo":"https://github.com/LZleejean/FutureHouse","url":"https://github.com/LZleejean/FutureHouse","frameworks":["pytorch"]}],"num_papers_in_archive":3,"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."}