{"url":"/dataset/3d60","name":"3D60","full_name":null,"description_markdown":"Collects high quality 360 datasets with ground truth depth annotations, by re-using recently released large scale 3D datasets and re-purposing them to 360 via rendering. \r\n\r\nSource: [OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas](/paper/omnidepth-dense-depth-estimation-for-indoors)","description_withheld":null,"homepage":"https://vcl3d.github.io/3D60/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/omnidepth-dense-depth-estimation-for-indoors","title":"OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas","first_author":"Nikolaos Zioulis","url":null},"license":null,"modalities":[],"tasks":[{"name":"Depth Estimation","url":"/task/depth-estimation","datasets_with_task":"/datasets/task/depth-estimation"},{"name":"Monocular Depth Estimation","url":"/task/monocular-depth-estimation","datasets_with_task":"/datasets/task/monocular-depth-estimation"}],"languages":[],"variants":["3D60"],"data_loaders":[{"repo":"https://github.com/meder411/OmniDepth-PyTorch","url":"https://github.com/meder411/OmniDepth-PyTorch","frameworks":["pytorch"]}],"num_papers_in_archive":18,"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."}