{"url":"/dataset/interiorverse","name":"Interiorverse","full_name":null,"description_markdown":"**Interiorverse** is a high-quality indoor scene dataset with rich details, including complex furniture and decorations and it is rendered with GGX BRDF model, which has stronger material modeling capability than any BRDF models.\r\n\r\nSource: [Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing](https://arxiv.org/pdf/2211.03017v1.pdf)\r\n\r\nImage Source: [https://arxiv.org/pdf/2211.03017v1.pdf](https://arxiv.org/pdf/2211.03017v1.pdf)","description_withheld":null,"homepage":"https://jingsenzhu.github.io/invrend/#","introduced_date":"2022-11-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/learning-based-inverse-rendering-of-complex","title":"Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing","first_author":"Jingsen Zhu","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[],"languages":[],"variants":["Interiorverse"],"data_loaders":[],"num_papers_in_archive":7,"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."}