解决3D异常检测中多类别特征混淆问题，提升检测可靠性
A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection
- 通过解耦语义表示，让模型在重建时更准确捕捉不同类别的特征
- 在Real3D-AD和Anomaly-ShapeNet上，物体级AUROC分别提升2.8%和9.1%
- 适合需要高可靠性的工业缺陷检测场景
3D异常检测旨在仅用正常数据训练的条件下，识别和定位3D点云中的缺陷。虽然统一模型通过跨类别学习提升了可扩展性，但常面临类别间语义纠缠（ICE）问题——不同类别的潜在特征重叠，导致模型在重建时采用错误的语义先验，最终产生不可靠的异常分数。为此，我们提出语义解耦的统一3D异常检测模型，其特征重建基于解耦的语义表示。框架包含三个核心组件：(i) 从粗到细的全局标记化，构建实例级语义身份；(ii) 类别条件对比学习，实现类别语义解耦；(iii) 几何引导解码器，确保语义一致的重建。在Real3D-AD和Anomaly-ShapeNet上的大量实验表明，该方法在统一模型和特定类别模型上均达到当前最优性能，物体级AUROC分别提升2.8%和9.1%，显著增强了统一3D异常检测的可靠性。
原文摘要 · Abstract (English)
3D anomaly detection targets the detection and localization of defects in 3D point clouds trained solely on normal data. While a unified model improves scalability by learning across multiple categories, it often suffers from Inter-Category Entanglement (ICE)-where latent features from different categories overlap, causing the model to adopt incorrect semantic priors during reconstruction and ultimately yielding unreliable anomaly scores. To address this issue, we propose the Semantically Disentangled Unified Model for 3D Anomaly Detection, which reconstructs features conditioned on disentangled semantic representations. Our framework consists of three key components: (i) Coarse-to-Fine Global Tokenization for forming instance-level semantic identity, (ii) Category-Conditioned Contrastive Learning for disentangling category semantics, and (iii) a Geometry-Guided Decoder for semantically consistent reconstruction. Extensive experiments on Real3D-AD and Anomaly-ShapeNet demonstrate that our method achieves state-of-the-art for both unified and category-specific models, improving object-level AUROC by 2.8% and 9.1%, respectively, while enhancing the reliability of unified 3D anomaly detection.
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