{"url":"/dataset/real-3d-ad","name":"Real 3D-AD","full_name":null,"description_markdown":"Real 3D-AD is the first point cloud anomaly detection dataset for industrial products.\r\nReal3D-AD comprises a total of 1,254 samples that are distributed across 12 distinct categories. These categories include Airplane, Car, Candybar, Chicken, Diamond, Duck, Fish, Gemstone, Seahorse, Shell, Starfish, and Toffees.\r\nEach training sample is an absence of blind spots, and a realistic, high-accuracy prototype.","description_withheld":null,"homepage":"https://github.com/M-3LAB/Real3D-AD","introduced_date":"2023-09-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/real3d-ad-a-dataset-of-point-cloud-anomaly-1","title":"Real3D-AD: A Dataset of Point Cloud Anomaly Detection","first_author":"Jiaqi Liu","url":null},"license":{"name":"The dataset is released under the CC BY 4.0 license.","url":null},"modalities":[{"name":"3D","url":"/datasets/modality/3d"},{"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","url":"/task/3d-anomaly-detection","datasets_with_task":"/datasets/task/3d-anomaly-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["Real 3D-AD"],"data_loaders":[],"num_papers_in_archive":23,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-anomaly-detection-on-real-3d-ad","task":"3D Anomaly Detection","dataset_variant":"Real 3D-AD","rows":19,"metrics":["Mean Performance of P. and O. ","Point AUROC","Object AUROC"],"first_row_in_archive_order":{"model":"DUS-Net","paper":"/paper/taming-anomalies-with-down-up-sampling","metrics":{"Mean Performance of P. and O. ":"0.821","Object AUROC":"0.795","Point AUROC":"0.847"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/anomaly-detection-on-real-3d-ad","task":"Anomaly Detection","dataset_variant":"Real 3D-AD","rows":1,"metrics":["Object AUROC","Point AUPR"],"first_row_in_archive_order":{"model":"Reg 3D-AD","paper":null,"metrics":{"Object AUROC":"0.704","Point AUPR":"0.109"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/taming-anomalies-with-down-up-sampling","title":"Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection","date":"2025-07-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/bridging-3d-anomaly-localization-and-repair","title":"Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation","date":"2025-05-30","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"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/mc3d-ad-a-unified-geometry-aware","title":"MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection","date":"2025-05-04","rows_on_this_dataset":1,"code_links":0,"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/look-inside-for-more-internal-spatial","title":"Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection","date":"2024-12-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/po3ad-predicting-point-offsets-toward-better","title":"PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection","date":"2024-12-17","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/pointad-comprehending-3d-anomalies-from","title":"PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection","date":"2024-10-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":26,"samples_ran":17,"samples_unverified":9,"pointer_only_for_licence":23,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/towards-high-resolution-3d-anomaly-detection","title":"Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning","date":"2024-08-08","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/r3d-ad-reconstruction-via-diffusion-for-3d","title":"R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection","date":"2024-07-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/towards-scalable-3d-anomaly-detection-and","title":"Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network","date":"2023-11-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/real3d-ad-a-dataset-of-point-cloud-anomaly-1","title":"Real3D-AD: A Dataset of Point Cloud Anomaly Detection","date":"2023-09-23","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":2,"samples_unverified":3,"pointer_only_for_licence":5,"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":2,"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/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":2,"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/towards-total-recall-in-industrial-anomaly","title":"Towards Total Recall in Industrial Anomaly Detection","date":"2021-06-15","rows_on_this_dataset":3,"code_links":18,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":36,"samples_ran":5,"samples_unverified":31,"pointer_only_for_licence":0,"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":5,"samples_harvested":85,"samples_ran":28,"samples_unverified":57,"pointer_only_for_licence":29,"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."}