C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor

TL;DR

This paper introduces C3D-AD, a continual learning framework for 3D anomaly detection that effectively learns from multiple classes and adapts to new categories over time using kernel attention and a learnable advisor.

Contribution

The paper proposes a novel continual learning framework with kernel attention modules and a representation rehearsal loss for 3D anomaly detection, enabling multi-class learning and adaptation to new classes.

Findings

Achieved average AUROC of 66.4%, 83.1%, and 63.4% on three datasets.

Effectively learns generalized representations for multiple classes.

Successfully handles emerging classes over time.

Abstract

3D Anomaly Detection (AD) has shown great potential in detecting anomalies or defects of high-precision industrial products. However, existing methods are typically trained in a class-specific manner and also lack the capability of learning from emerging classes. In this study, we proposed a continual learning framework named Continual 3D Anomaly Detection (C3D-AD), which can not only learn generalized representations for multi-class point clouds but also handle new classes emerging over time.Specifically, in the feature extraction module, to extract generalized local features from diverse product types of different tasks efficiently, Kernel Attention with random feature Layer (KAL) is introduced, which normalizes the feature space. Then, to reconstruct data correctly and continually, an efficient Kernel Attention with learnable Advisor (KAA) mechanism is proposed, which learns the information from new categories while discarding redundant old information within both the encoder and decoder. Finally, to keep the representation consistency over tasks, a Reconstruction with Parameter Perturbation (RPP) module is proposed by designing a representation rehearsal loss function, which ensures that the model remembers previous category information and returns category-adaptive representation.Extensive experiments on three public datasets demonstrate the effectiveness of the proposed method, achieving an average performance of 66.4%, 83.1%, and 63.4% AUROC on Real3D-AD, Anomaly-ShapeNet, and MulSen-AD, respectively.