{"url":"/dataset/amt-objects","name":"AMT Objects","full_name":null,"description_markdown":"AMT Objects is a large dataset of object centric videos suitable for training and benchmarking models for generating 3D models of objects from a small number of photos of the objects. The dataset consists of multiple views of a large collection of object instances.\r\n\r\nThe dataset contains 7 object categories from the MS COCO classes: apple, sandwich, orange, donut, banana, carrot and hydrant. For each class, annotators were asked to collect a video by looking ‘around’ a class instance, resulting in a turntable video. The dataset contains 169-457 videos per class. For each class, the videos were randomly split into training and testing videos in an 8:1 ratio.","description_withheld":null,"homepage":"https://henzler.github.io/publication/unsupervised_videos/","introduced_date":"2021-03-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/unsupervised-learning-of-3d-object-categories","title":"Unsupervised Learning of 3D Object Categories from Videos in the Wild","first_author":"Philipp Henzler","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"3D Reconstruction","url":"/task/3d-reconstruction","datasets_with_task":"/datasets/task/3d-reconstruction"}],"languages":[],"variants":["AMT Objects"],"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."}