{"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/mc3d-ad-a-unified-geometry-aware","title":"MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection","arxiv_id":"2505.01969","date":"2025-05-04","proceeding":null,"authors":["Jiayi Cheng","Can Gao","Jie zhou","Jiajun Wen","Tao Dai","Jinbao Wang"],"abstract":"3D Anomaly Detection (AD) is a promising means of controlling the quality of manufactured products. However, existing methods typically require carefully training a task-specific model for each category independently, leading to high cost, low efficiency, and weak generalization. Therefore, this paper presents a novel unified model for Multi-Category 3D Anomaly Detection (MC3D-AD) that aims to utilize both local and global geometry-aware information to reconstruct normal representations of all categories. First, to learn robust and generalized features of different categories, we propose an adaptive geometry-aware masked attention module that extracts geometry variation information to guide mask attention. Then, we introduce a local geometry-aware encoder reinforced by the improved mask attention to encode group-level feature tokens. Finally, we design a global query decoder that utilizes point cloud position embeddings to improve the decoding process and reconstruction ability. This leads to local and global geometry-aware reconstructed feature tokens for the AD task. MC3D-AD is evaluated on two publicly available Real3D-AD and Anomaly-ShapeNet datasets, and exhibits significant superiority over current state-of-the-art single-category methods, achieving 3.1\\% and 9.3\\% improvement in object-level AUROC over Real3D-AD and Anomaly-ShapeNet, respectively. The source code will be released upon acceptance.","url_abs":"https://arxiv.org/abs/2505.01969v1","url_pdf":"https://arxiv.org/pdf/2505.01969v1.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":[],"tasks":[{"task_slug":"3d-anomaly-detection","task_name":"3D Anomaly Detection"},{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-anomaly-detection-on-anomaly-shapenet","task":"3D Anomaly Detection","dataset":"Anomaly-ShapeNet","model":"MC3D-AD","rank_in_archive_order":3,"of":8,"metrics":{"O-AUROC":"0.842"},"uses_additional_data":false},{"leaderboard":"/sota/3d-anomaly-detection-on-real-3d-ad","task":"3D Anomaly Detection","dataset":"Real 3D-AD","model":"MC3D-AD","rank_in_archive_order":4,"of":19,"metrics":{"Mean Performance of P. and O. ":"0.775","Object AUROC":"0.782","Point AUROC":"0.768"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2505.01969","atlas_url":"https://app.syntology.ai/?focus=2505.01969","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}