Multimodal Industrial Anomaly Detection via Geometric Prior
The purpose of multimodal industrial anomaly detection is to detect complex geometric shape defects such as subtle surface deformations and irregular contours that are difficult to detect in 2D-based methods. However, current multimodal industrial anomaly detection lacks the effective use of crucial geometric information like surface normal vectors and 3D shape topology, resulting in low detection accuracy. In this paper, we propose a novel Geometric Prior-based Anomaly Detection network (GPAD). Firstly, we propose a point cloud expert model to perform fine-grained geometric feature extraction, employing differential normal vector computation to enhance the geometric details of the extrac...
| Authors | Min Li, Jinghui He, Gang Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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