{"url":"/dataset/dense","name":"DENSE","full_name":"Depth Estimation oN Synthetic Events","description_markdown":"DENSE (Depth Estimation oN Synthetic Events) is a new dataset with synthetic events and perfect ground truth.\r\n\r\nSource: [Learning Monocular Dense Depth from Events](http://rpg.ifi.uzh.ch/docs/3DV20_Hidalgo.pdf)","description_withheld":null,"homepage":"https://github.com/uzh-rpg/rpg_e2depth","introduced_date":"2020-10-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/learning-monocular-dense-depth-from-events","title":"Learning Monocular Dense Depth from Events","first_author":"Javier Hidalgo-Carrió","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Depth Estimation","url":"/task/depth-estimation","datasets_with_task":"/datasets/task/depth-estimation"},{"name":"Image Dehazing","url":"/task/image-dehazing","datasets_with_task":"/datasets/task/image-dehazing"},{"name":"Probabilistic Deep Learning","url":"/task/probabilistic-deep-learning","datasets_with_task":"/datasets/task/probabilistic-deep-learning"}],"languages":[],"variants":["Dense-Haze","DENSE"],"data_loaders":[{"repo":"https://github.com/uzh-rpg/rpg_e2depth","url":"https://github.com/uzh-rpg/rpg_e2depth","frameworks":["pytorch"]}],"num_papers_in_archive":49,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-dehazing-on-dense-haze","task":"Image Dehazing","dataset_variant":"Dense-Haze","rows":5,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"CasDyF-Net","paper":"/paper/casdyf-net-image-dehazing-via-cascaded","metrics":{"PSNR":"17.56","SSIM":"0.658"},"code_links":[{"title":"dauing/casdyf-net","url":"https://github.com/dauing/casdyf-net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sad-net-a-full-spectral-self-attention-detail","title":"SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing","date":"2025-04-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/casdyf-net-image-dehazing-via-cascaded","title":"CasDyF-Net: Image Dehazing via Cascaded Dynamic Filters","date":"2024-09-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/structure-representation-network-and","title":"Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal","date":"2022-10-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-novel-encoder-decoder-network-with-guided","title":"A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing","date":"2022-02-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/single-image-dehazing-for-a-variety-of-haze","title":"Single image dehazing for a variety of haze scenarios using back projected pyramid network","date":"2020-08-15","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":1,"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."}