Uni-3DAD: Gan-inversion aided universal 3D anomaly detection on model-free products

Anomaly detection is a long-standing challenge in manufacturing systems, aiming to locate surface defects and improve product quality. Traditionally, anomaly detection has relied on human inspectors or image-based methods. 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”, “Bagel”, “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, unlike missing regions in images, which manifest as different pixel values or patterns compared to a normal image patch, missing areas in 3D point clouds represent the absence of scanned points. This makes it infeasible to compare the missing region (with no representation in the recorded 3D Scan) with existing 3D point clouds 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 3D point clouds based state-of-the-art (SOTA) methods in identifying incomplete shapes and (2) it still maintains comparable performance with the SOTA methods in detecting all other types of anomalies.