{"url":"/dataset/shapestacks","name":"ShapeStacks","full_name":null,"description_markdown":"A simulation-based dataset featuring 20,000 stack configurations composed of a variety of elementary geometric primitives richly annotated regarding semantics and structural stability. \r\n\r\nSource: [ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking](https://arxiv.org/pdf/1804.08018v2.pdf)","description_withheld":null,"homepage":"https://arxiv.org/pdf/1804.08018v2.pdf","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/shapestacks-learning-vision-based-physical","title":"ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking","first_author":"Oliver Groth","url":null},"license":{"name":"Unknown","url":null},"modalities":[],"tasks":[{"name":"Image Generation","url":"/task/image-generation","datasets_with_task":"/datasets/task/image-generation"},{"name":"Scene Understanding","url":"/task/scene-understanding","datasets_with_task":"/datasets/task/scene-understanding"},{"name":"Unsupervised Object Segmentation","url":"/task/unsupervised-object-segmentation","datasets_with_task":"/datasets/task/unsupervised-object-segmentation"},{"name":"Object Discovery","url":"/task/object-discovery","datasets_with_task":"/datasets/task/object-discovery"}],"languages":[],"variants":["ShapeStacks"],"data_loaders":[],"num_papers_in_archive":22,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unsupervised-object-segmentation-on","task":"Unsupervised Object Segmentation","dataset_variant":"ShapeStacks","rows":5,"metrics":["ARI-FG"],"first_row_in_archive_order":{"model":"AST","paper":"/paper/unsupervised-multi-object-segmentation-using","metrics":{"ARI-FG":"0.82"},"code_links":[{"title":"BrunoSauvalle/AST","url":"https://github.com/BrunoSauvalle/AST"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-generation-on-shapestacks","task":"Image Generation","dataset_variant":"ShapeStacks","rows":3,"metrics":["FID"],"first_row_in_archive_order":{"model":"GENESIS-V2","paper":"/paper/genesis-v2-inferring-unordered-object","metrics":{"FID":"112.7"},"code_links":[{"title":"applied-ai-lab/genesis","url":"https://github.com/applied-ai-lab/genesis"},{"title":"jinyangyuan/genesis","url":"https://github.com/jinyangyuan/genesis"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/unsupervised-multi-object-segmentation-using","title":"Unsupervised Multi-object Segmentation Using Attention and Soft-argmax","date":"2022-05-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/genesis-v2-inferring-unordered-object","title":"GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement","date":"2021-04-20","rows_on_this_dataset":7,"code_links":2,"syntology":null}],"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."}