{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/omnidata-a-scalable-pipeline-for-making-multi-1","title":"Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans","arxiv_id":"2110.04994","date":"2021-10-11","proceeding":"ICCV 2021 10","authors":["Ainaz Eftekhar","Alexander Sax","Roman Bachmann","Jitendra Malik","Amir Zamir"],"abstract":"This paper introduces a pipeline to parametrically sample and render multi-task vision datasets from comprehensive 3D scans from the real world. Changing the sampling parameters allows one to \"steer\" the generated datasets to emphasize specific information. In addition to enabling interesting lines of research, we show the tooling and generated data suffice to train robust vision models. Common architectures trained on a generated starter dataset reached state-of-the-art performance on multiple common vision tasks and benchmarks, despite having seen no benchmark or non-pipeline data. The depth estimation network outperforms MiDaS and the surface normal estimation network is the first to achieve human-level performance for in-the-wild surface normal estimation -- at least according to one metric on the OASIS benchmark. The Dockerized pipeline with CLI, the (mostly python) code, PyTorch dataloaders for the generated data, the generated starter dataset, download scripts and other utilities are available through our project website, https://omnidata.vision.","url_abs":"https://arxiv.org/abs/2110.04994v1","url_pdf":"https://arxiv.org/pdf/2110.04994v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"omnidata-a-scalable-pipeline-for-making-multi-1","repo_url":"https://github.com/EPFL-VILAB/Omnidata","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"surface-normal-estimation","task_name":"Surface Normal Estimation"}],"methods":[{"method_slug":"oasis","method_name":"OASIS"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2110.04994","atlas_url":"https://app.syntology.ai/?focus=2110.04994","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.04994"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/EPFL-VILAB/Omnidata","reach":null}],"summary":{"ran_draft_wrong":1,"ran":1,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"01783e79c8ba3318","entry":"email_valid","repo":"EPFL-VILAB/Omnidata","repo_kind":"official","path":"omnidata_tools/dataset/download.py","file_url":"https://github.com/EPFL-VILAB/Omnidata/blob/HEAD/omnidata_tools/dataset/download.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"01783e79c8ba3318"}},{"code_sha256_prefix":"89ad4814501c48cc","entry":"prompt_name","repo":"EPFL-VILAB/Omnidata","repo_kind":"official","path":"omnidata_tools/dataset/download.py","file_url":"https://github.com/EPFL-VILAB/Omnidata/blob/HEAD/omnidata_tools/dataset/download.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"89ad4814501c48cc"}},{"code_sha256_prefix":"dc8b4e2d598536be","entry":"filter_models","repo":"EPFL-VILAB/Omnidata","repo_kind":"official","path":"omnidata_tools/dataset/download.py","file_url":"https://github.com/EPFL-VILAB/Omnidata/blob/HEAD/omnidata_tools/dataset/download.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"dc8b4e2d598536be"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}