C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with
  Learnable Advisor
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.
68942c64741a16f544fbcf39
Haoquan Lu
68942c64741a16f544fbcf3a
Hanzhe Liang
claimed_verified
2025-08-07T10:38:19.601Z
67840ec0dd05c4028915b606
/avatars/44e36e4e5febc024b28e258520dce175.svg
Hanzhe
user
HanzheL
68942c64741a16f544fbcf3b
Jie Zhang
68942c64741a16f544fbcf3c
Chenxi Hu
68942c64741a16f544fbcf3d
Jinbao Wang
68942c64741a16f544fbcf3e
Can Gao
C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with
  Learnable Advisor
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.
A continual learning framework for 3D anomaly detection uses Kernel Attention mechanisms and parameter perturbation to handle multiple and emerging classes of point clouds.
continual learning 3D anomaly detection C3D-AD Kernel Attention with random feature Layer KAL Kernel Attention with learnable Advisor KAA Reconstruction with Parameter Perturbation RPP representation rehearsal loss function AUROC