{"url":"/dataset/mvtec-3d-ad","name":"MVTEC 3D-AD","full_name":"THE MVTEC 3D ANOMALY DETECTION DATASET","description_markdown":"MVTec 3D Anomaly Detection Dataset (MVTec 3D-AD) is a comprehensive 3D dataset for the task of unsupervised anomaly detection and localization. It contains over 4000 high-resolution scans acquired by an industrial 3D sensor. Each of the 10 different object categories comprises a set of defect-free training and validation samples and a test set of samples with various kinds of defects. Precise ground-truth annotations are provided for each anomalous test sample.","description_withheld":null,"homepage":"https://www.mvtec.com/company/research/datasets/mvtec-3d-ad","introduced_date":"2021-12-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-mvtec-3d-ad-dataset-for-unsupervised-3d","title":"The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization","first_author":"Paul Bergmann","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"3D Anomaly Detection and Segmentation","url":"/task/3d-anomaly-detection-and-segmentation","datasets_with_task":"/datasets/task/3d-anomaly-detection-and-segmentation"},{"name":"Depth Anomaly Detection and Segmentation","url":"/task/depth-anomaly-detection-and-segmentation","datasets_with_task":"/datasets/task/depth-anomaly-detection-and-segmentation"},{"name":"RGB+3D Anomaly Detection and Segmentation","url":"/task/rgb-3d-anomaly-detection-and-segmentation","datasets_with_task":"/datasets/task/rgb-3d-anomaly-detection-and-segmentation"}],"languages":[],"variants":["MVTEC 3D-AD"],"data_loaders":[],"num_papers_in_archive":45,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/depth-anomaly-detection-and-segmentation-on","task":"Depth Anomaly Detection and Segmentation","dataset_variant":"MVTEC 3D-AD","rows":13,"metrics":["Segmentation AUPRO","Detection AUROC","Segmentation AUROC"],"first_row_in_archive_order":{"model":"TransFusion","paper":"/paper/transfusion-a-transparency-based-diffusion","metrics":{"Detection AUROC":"0.957","Segmentation AUPRO":"0.947"},"code_links":[{"title":"maticfuc/eccv_transfusion","url":"https://github.com/maticfuc/eccv_transfusion"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/3d-anomaly-detection-and-segmentation-on","task":"3D Anomaly Detection and Segmentation","dataset_variant":"MVTEC 3D-AD","rows":11,"metrics":["Detection AUROC","Segmentation AUROC"],"first_row_in_archive_order":{"model":"MC4AD","paper":"/paper/examining-the-source-of-defects-from-a","metrics":{"Detection AUROC":"0.954","Segmentation AUROC":"0.946"},"code_links":[{"title":"hzzzzzhappy/mc4ad","url":"https://github.com/hzzzzzhappy/mc4ad"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/rgb-3d-anomaly-detection-and-segmentation-on","task":"RGB+3D Anomaly Detection and Segmentation","dataset_variant":"MVTEC 3D-AD","rows":9,"metrics":["Detection AUCROC","Segmentation AUPRO","Segmentation AUCROC"],"first_row_in_archive_order":{"model":"TransFusion","paper":"/paper/transfusion-a-transparency-based-diffusion","metrics":{"Detection AUCROC":"0.982","Segmentation AUPRO":"0.983"},"code_links":[{"title":"maticfuc/eccv_transfusion","url":"https://github.com/maticfuc/eccv_transfusion"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/anomaly-detection-on-mvtec-3d-ad-1","task":"Anomaly Detection","dataset_variant":"MVTEC 3D-AD","rows":2,"metrics":["Segmentation AUPRO","Detection AUROC","Segmentation AUROC"],"first_row_in_archive_order":{"model":"CDO","paper":"/paper/collaborative-discrepancy-optimization-for-1","metrics":{"Segmentation AUPRO":"93.75"},"code_links":[{"title":"caoyunkang/CDO","url":"https://github.com/caoyunkang/CDO"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/examining-the-source-of-defects-from-a","title":"Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection","date":"2025-05-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/boosting-global-local-feature-matching-via","title":"Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection","date":"2025-02-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/transfusion-a-transparency-based-diffusion","title":"TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection","date":"2023-11-16","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cheating-depth-enhancing-3d-surface-anomaly","title":"Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation","date":"2023-11-02","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/complementary-pseudo-multimodal-feature-for","title":"Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection","date":"2023-03-23","rows_on_this_dataset":6,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multimodal-industrial-anomaly-detection-via","title":"Multimodal Industrial Anomaly Detection via Hybrid Fusion","date":"2023-03-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":3,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/collaborative-discrepancy-optimization-for-1","title":"Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization","date":"2023-02-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":3,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/shape-guided-shape-guided-dual-memory","title":"Shape-Guided: Shape-Guided Dual-Memory Learning for 3D Anomaly Detection","date":"2023-01-27","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/asymmetric-student-teacher-networks-for","title":"Asymmetric Student-Teacher Networks for Industrial Anomaly Detection","date":"2022-10-14","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/an-empirical-investigation-of-3d-anomaly","title":"Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection","date":"2022-03-10","rows_on_this_dataset":7,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":1,"samples_unverified":8,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/anomaly-detection-in-3d-point-clouds-using","title":"Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors","date":"2022-02-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/the-mvtec-3d-ad-dataset-for-unsupervised-3d","title":"The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization","date":"2021-12-16","rows_on_this_dataset":9,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":6,"samples_harvested":28,"samples_ran":10,"samples_unverified":18,"pointer_only_for_licence":1,"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."}