{"url":"/dataset/pano3d","name":"Pano3D","full_name":null,"description_markdown":"Pano3D  is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner.  The Pano3D 360 depth estimation benchmark provides a standard Matterport3D train and test split, as well as a secondary GibsonV2 partioning for testing and training as well. The latter is used for zero-shot cross dataset transfer performance assessment and decomposes it into 3 different splits, each one focusing on a specific generalization axis.","description_withheld":null,"homepage":"https://vcl3d.github.io/Pano3D","introduced_date":"2021-09-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/pano3d-a-holistic-benchmark-and-a-solid","title":"Pano3D: A Holistic Benchmark and a Solid Baseline for $360^o$ Depth Estimation","first_author":"Georgios Albanis","url":null},"license":{"name":"Custom","url":"https://vcl3d.github.io/Pano3D/download/#Download"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"},{"name":"Depth Estimation","url":"/task/depth-estimation","datasets_with_task":"/datasets/task/depth-estimation"},{"name":"Out-of-Distribution Detection","url":"/task/out-of-distribution-detection","datasets_with_task":"/datasets/task/out-of-distribution-detection"},{"name":"3D Reconstruction","url":"/task/3d-reconstruction","datasets_with_task":"/datasets/task/3d-reconstruction"},{"name":"Surface Normals Estimation","url":"/task/surface-normals-estimation","datasets_with_task":"/datasets/task/surface-normals-estimation"},{"name":"3D Depth Estimation","url":"/task/3d-depth-estimation","datasets_with_task":"/datasets/task/3d-depth-estimation"},{"name":"Zero-Shot Learning + Domain Generalization","url":"/task/zero-shot-learning-domain-generalization","datasets_with_task":"/datasets/task/zero-shot-learning-domain-generalization"},{"name":"Zero-Shot Out-of-Domain Detection","url":"/task/zero-shot-out-of-domain-detection","datasets_with_task":"/datasets/task/zero-shot-out-of-domain-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Pano3D"],"data_loaders":[{"repo":"https://github.com/VCL3D/Pano3D","url":"https://vcl3d.github.io/Pano3D/download/","frameworks":["pytorch"]}],"num_papers_in_archive":2,"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."}