Uni-3DAD：基于GAN逆变换的无模型产品通用三维异常检测
Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on Model-free Products
AI总结：
提出Uni-3DAD统一无监督三维异常检测框架，结合特征与重建模块并用OCSVM融合，能检测无模型产品上包括缺失在内的所有缺陷类型，性能优于现有方法。
摘要：
异常检测是制造系统中长期存在的挑战。传统上，异常检测依赖于人工检查员。然而，三维点云因其对环境因素的鲁棒性以及表示几何数据的能力而受到关注。现有的三维异常检测方法通常分为两类。一类将扫描的三维点云与设计文件进行比较，假设这些文件始终可用。然而，在许多实际应用中，这种假设经常被违反，因为存在无模型产品，如新鲜农产品（即“饼干”、“土豆”等）、假牙、骨骼等。另一类将扫描的三维点云的补丁与名为记忆库的正常补丁库进行比较。然而，这些方法通常无法检测不完整形状，这是一种相当常见的缺陷类型（即不同产品的缺失部分）。主要挑战在于三维点云中的缺失区域代表扫描点的缺失。这使得将缺失区域与记忆库中现有的点云补丁进行比较变得不可行。为了解决这两个挑战，我们提出了一个统一的、无监督的三维异常检测框架，能够识别无模型产品上的所有类型的缺陷。我们的方法集成了两个检测模块：基于特征的检测模块和基于重建的检测模块。基于特征的检测涵盖几何缺陷，如凹痕、孔洞和裂缝，而基于重建的方法检测缺失区域。此外，我们采用一类支持向量机（OCSVM）来融合两个模块的检测结果。结果表明：（1）我们提出的方法在识别不完整形状方面优于最先进的方法；（2）在检测所有其他类型的异常方面，它仍保持与最先进方法相当的性能。
英文摘要：
Anomaly detection is a long-standing challenge in manufacturing systems. Traditionally, anomaly detection has relied on human inspectors. However, 3D point clouds have gained attention due to their robustness to environmental factors and their ability to represent geometric data. Existing 3D anomaly detection methods generally fall into two categories. One compares scanned 3D point clouds with design files, assuming these files are always available. However, such assumptions are often violated in many real-world applications where model-free products exist, such as fresh produce (i.e., ``Cookie", ``Potato", etc.), dentures, bone, etc. The other category compares patches of scanned 3D point clouds with a library of normal patches named memory bank. However, those methods usually fail to detect incomplete shapes, which is a fairly common defect type (i.e., missing pieces of different products). The main challenge is that missing areas in 3D point clouds represent the absence of scanned points. This makes it infeasible to compare the missing region with existing point cloud patches in the memory bank. To address these two challenges, we proposed a unified, unsupervised 3D anomaly detection framework capable of identifying all types of defects on model-free products. Our method integrates two detection modules: a feature-based detection module and a reconstruction-based detection module. Feature-based detection covers geometric defects, such as dents, holes, and cracks, while the reconstruction-based method detects missing regions. Additionally, we employ a One-class Support Vector Machine (OCSVM) to fuse the detection results from both modules. The results demonstrate that (1) our proposed method outperforms the state-of-the-art methods in identifying incomplete shapes and (2) it still maintains comparable performance with the SOTA methods in detecting all other types of anomalies.
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。
