用连续几何表示同时实现3D异常定位与修复
Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation
- 通过姿态对齐与SDF网络学习连续形状表征
- 在Real3D-AD和Anomaly-ShapeNet上分别达80.2%和90.0%的物体级AUROC
- 支持像素级定位与原位修复，适合工业质检场景
3D点云异常检测对构建鲁棒视觉系统至关重要，但受姿态变化和复杂几何异常挑战。现有基于补丁的方法因离散体素化或投影表示导致几何保真度下降，难以实现细粒度异常定位。本文提出姿态感知符号距离场（PASDF），通过姿态对齐模块归一化形状，并利用SDF网络动态融合姿态信息，隐式学习高保真异常修复模板。通过异常感知评分模块实现像素级异常定位。关键在于，连续3D表示不仅用于检测，还可直接支持原位异常修复。在Real3D-AD和Anomaly-ShapeNet数据集上达到80.2%和90.0%的物体级AUROC，显著优于现有方法。代码已开源，推动后续研究。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。