{"url":"/dataset/diml-cvl-rgb-d-dataset","name":"DIML/CVl RGB-D Dataset","full_name":null,"description_markdown":"This dataset contains synchronized RGB-D frames from both Kinect v2 and Zed stereo camera. For the outdoor scene, the authors first generate disparity maps using an accurate stereo matching method and convert them using calibration parameters. A per-pixel confidence map of disparity is also provided. The scenes are captured at various places, e.g., offices, rooms, dormitory, exhibition center, street, road etc., from Yonsei University and Ewha University.\r\n\r\nSource: [DIML/CVl RGB-D Dataset](https://dimlrgbd.github.io/)","description_withheld":null,"homepage":"https://dimlrgbd.github.io/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["DIML/CVl RGB-D Dataset"],"data_loaders":[],"num_papers_in_archive":4,"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."}