{"url":"/dataset/objaverse","name":"Objaverse","full_name":null,"description_markdown":"**Objaverse** is a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category.\r\n\r\nSource: [Objaverse: A Universe of Annotated 3D Objects](https://arxiv.org/pdf/2212.08051v1.pdf)\r\n\r\nImage Source: [https://objaverse.allenai.org/](https://objaverse.allenai.org/)","description_withheld":null,"homepage":"https://objaverse.allenai.org/","introduced_date":"2022-12-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/objaverse-a-universe-of-annotated-3d-objects","title":"Objaverse: A Universe of Annotated 3D Objects","first_author":"Matt Deitke","url":null},"license":null,"modalities":[{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"Zero-shot 3D classification","url":"/task/zero-shot-3d-classification","datasets_with_task":"/datasets/task/zero-shot-3d-classification"},{"name":"Generative 3D Object Classification","url":"/task/generative-3d-object-classification","datasets_with_task":"/datasets/task/generative-3d-object-classification"},{"name":"3D Object Captioning","url":"/task/3d-object-captioning","datasets_with_task":"/datasets/task/3d-object-captioning"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Objaverse","Objaverse LVIS"],"data_loaders":[],"num_papers_in_archive":393,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/generative-3d-object-classification-on-1","task":"Generative 3D Object Classification","dataset_variant":"Objaverse","rows":7,"metrics":["Objaverse (Average)","Objaverse (I)","Objaverse (C)"],"first_row_in_archive_order":{"model":"MiniGPT-3D","paper":"/paper/minigpt-3d-efficiently-aligning-3d-point","metrics":{"Objaverse (Average)":"60.25","Objaverse (C)":"60.50","Objaverse (I)":"60.00"},"code_links":[{"title":"tangyuan96/minigpt-3d","url":"https://github.com/tangyuan96/minigpt-3d"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/3d-object-captioning-on-objaverse-1","task":"3D Object Captioning","dataset_variant":"Objaverse","rows":6,"metrics":["GPT-4"," Sentence-BERT","SimCSE","Precision","Correctness","Hallucination"],"first_row_in_archive_order":{"model":"MiniGPT-3D","paper":"/paper/minigpt-3d-efficiently-aligning-3d-point","metrics":{" Sentence-BERT":"49.54","Correctness":"3.50","GPT-4":"57.06","Hallucination":"0.71","Precision":"83.14","SimCSE":"51.39"},"code_links":[{"title":"tangyuan96/minigpt-3d","url":"https://github.com/tangyuan96/minigpt-3d"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/minigpt-3d-efficiently-aligning-3d-point","title":"MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors","date":"2024-05-02","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/shapellm-universal-3d-object-understanding","title":"ShapeLLM: Universal 3D Object Understanding for Embodied Interaction","date":"2024-02-27","rows_on_this_dataset":4,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":9,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/point-bind-point-llm-aligning-point-cloud","title":"Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following","date":"2023-09-01","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":13,"samples_unverified":7,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pointllm-empowering-large-language-models-to","title":"PointLLM: Empowering Large Language Models to Understand Point Clouds","date":"2023-08-31","rows_on_this_dataset":4,"code_links":3,"syntology":null},{"paper":"/paper/3d-llm-injecting-the-3d-world-into-large","title":"3D-LLM: Injecting the 3D World into Large Language Models","date":"2023-07-24","rows_on_this_dataset":2,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":5,"samples_unverified":5,"pointer_only_for_licence":10,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":47,"samples_ran":27,"samples_unverified":20,"pointer_only_for_licence":17,"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."}