具有自适应补丁代码书的分层点-补丁融合用于3D形状异常检测
Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection
AI总结：
本文提出一种分层点-补丁异常评分网络，通过联合建模区域部分特征和局部点特征，提升3D形状异常检测的鲁棒性。采用自适应补丁化模块捕捉复杂结构偏差，并在公开和工业数据集上取得优越的AUC-ROC和AUC-PR性能。
摘要：
3D形状异常检测是工业检测和几何分析中的关键任务。现有深度学习方法通常通过学习正常形状的表示并利用分布外特征检测或解码器基于重建来识别异常。它们在跨多样异常类型和尺度（如全局几何误差（例如平面位移、角度错位））时泛化能力不足，并且在训练过程中对噪声或不完整的局部点敏感。为了解决这些限制，我们提出了一种分层点-补丁异常评分网络，该网络联合建模区域部分特征和局部点特征，以实现稳健的异常推理。一个自适应补丁化模块集成了自监督分解，以捕捉复杂的结构偏差。除了在公开基准（Anomaly-ShapeNet和Real3D-AD）上的评估外，我们还发布了一个包含真实CAD模型的工业测试集，这些模型表现出平面、角度和结构缺陷。在公开和工业数据集上的实验显示，该方法在AUC-ROC和AUC-PR性能上均优于现有方法，包括在新的工业异常类型上点级改进超过40%，在Real3D-AD上平均对象级提升7%，在Anomaly-ShapeNet上平均提升4%，表明了强大的鲁棒性和泛化能力。
英文摘要：
3D shape anomaly detection is a crucial task for industrial inspection and geometric analysis. Existing deep learning approaches typically learn representations of normal shapes and identify anomalies via out-of-distribution feature detection or decoder-based reconstruction. They often fail to generalize across diverse anomaly types and scales, such as global geometric errors (e.g., planar shifts, angle misalignments), and are sensitive to noisy or incomplete local points during training. To address these limitations, we propose a hierarchical point-patch anomaly scoring network that jointly models regional part features and local point features for robust anomaly reasoning. An adaptive patchification module integrates self-supervised decomposition to capture complex structural deviations. Beyond evaluations on public benchmarks (Anomaly-ShapeNet and Real3D-AD), we release an industrial test set with real CAD models exhibiting planar, angular, and structural defects. Experiments on public and industrial datasets show superior AUC-ROC and AUC-PR performance, including over 40% point-level improvement on the new industrial anomaly type and average object-level gains of 7% on Real3D-AD and 4% on Anomaly-ShapeNet, demonstrating strong robustness and generalization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
