{"url":"/dataset/foggy-kitti","name":"Foggy KITTI","full_name":null,"description_markdown":"The Foggy KITTI dataset extends the KITTI dataset to include challenging weather conditions, aiming to support research in real-world applications such as autonomous driving. It contains synthetic fog images with different levels of intensity and is divided into training and testing sets, providing a useful resource for developing and evaluating models in practical scenarios.","description_withheld":null,"homepage":"https://github.com/VisualAIKHU/MonoWAD","introduced_date":"2024-07-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/monowad-weather-adaptive-diffusion-model-for","title":"MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection","first_author":"Youngmin Oh","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[],"languages":[],"variants":["Foggy KITTI"],"data_loaders":[],"num_papers_in_archive":1,"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."}