{"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/bridging-3d-anomaly-localization-and-repair","title":"Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation","arxiv_id":"2505.24431","date":"2025-05-30","proceeding":null,"authors":["Bozhong Zheng","Jinye Gan","Xiaohao Xu","Wenqiao Li","Xiaonan Huang","Na Ni","Yingna Wu"],"abstract":"3D point cloud anomaly detection is essential for robust vision systems but is challenged by pose variations and complex geometric anomalies. Existing patch-based methods often suffer from geometric fidelity issues due to discrete voxelization or projection-based representations, limiting fine-grained anomaly localization. We introduce Pose-Aware Signed Distance Field (PASDF), a novel framework that integrates 3D anomaly detection and repair by learning a continuous, pose-invariant shape representation. PASDF leverages a Pose Alignment Module for canonicalization and a SDF Network to dynamically incorporate pose, enabling implicit learning of high-fidelity anomaly repair templates from the continuous SDF. This facilitates precise pixel-level anomaly localization through an Anomaly-Aware Scoring Module. Crucially, the continuous 3D representation in PASDF extends beyond detection, facilitating in-situ anomaly repair. Experiments on Real3D-AD and Anomaly-ShapeNet demonstrate state-of-the-art performance, achieving high object-level AUROC scores of 80.2% and 90.0%, respectively. These results highlight the effectiveness of continuous geometric representations in advancing 3D anomaly detection and facilitating practical anomaly region repair. The code is available at https://github.com/ZZZBBBZZZ/PASDF to support further research.","url_abs":"https://arxiv.org/abs/2505.24431v1","url_pdf":"https://arxiv.org/pdf/2505.24431v1.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"},{"task_slug":"anomaly-localization","task_name":"Anomaly Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-anomaly-detection-on-anomaly-shapenet","task":"3D Anomaly Detection","dataset":"Anomaly-ShapeNet","model":"PASDF","rank_in_archive_order":2,"of":8,"metrics":{"O-AUROC":"0.900","P-AUROC":"0.897"},"uses_additional_data":false},{"leaderboard":"/sota/3d-anomaly-detection-on-real-3d-ad","task":"3D Anomaly Detection","dataset":"Real 3D-AD","model":"PASDF","rank_in_archive_order":5,"of":19,"metrics":{"Mean Performance of P. and O. ":"0.7735","Object AUROC":"0.802","Point AUROC":"0.745"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2505.24431","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}