Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation

• URL: http://arxiv.org/abs/2505.24431v1
• Date: Fri, 30 May 2025 10:11:49 GMT
• Title: Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation
• Abstract summary: New framework integrates 3D anomaly detection and repair by learning a continuous, pose-invariant shape representation.<n>Experiments on Real3D-AD and Anomaly-ShapeNet demonstrate state-of-the-art performance.
• Score: 1.4763103835215192
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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