Abstract
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 extracted features and generate geometric prior. Secondly, we propose a two-stage fusion strategy to efficiently leverage the complementarity of multimodal data as well as the geometric prior inherent in 3D points. We further propose attention fusion and anomaly regions segmentation based on geometric prior, which enhance the model's ability to perceive geometric defects. Extensive experiments show that our multimodal industrial anomaly detection model outperforms the State-of-the-art (SOTA) methods in detection accuracy on both MVTec-3D AD and Eyecandies datasets.
Abstract (translated)
多模态工业异常检测旨在检测复杂的几何形状缺陷，例如基于二维方法难以检测的细微表面变形和不规则轮廓。然而，当前多模态工业异常检测缺乏对表面法向量和三维形状拓扑等关键几何信息的有效利用，导致检测精度较低。本文提出了一种新颖的基于几何先验的异常检测网络（GPAD）。首先，我们提出点云专家模型进行细粒度几何特征提取，采用微分法向量计算增强提取特征的几何细节并生成几何先验。其次，我们提出两阶段融合策略，以高效利用多模态数据的互补性以及三维点云固有的几何先验。我们还进一步提出了基于几何先验的注意力融合与异常区域分割，增强了模型对几何缺陷的感知能力。大量实验表明，我们的多模态工业异常检测模型在MVTec-3D AD和Eyecandies数据集上的检测精度均优于最先进（SOTA）方法。
URL
https://arxiv.org/abs/2603.22757
