基于局部感知图重构的点云异常检测网络

doi:10.16451/j.cnki.issn1003-6059.202511001

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Abstract

Point cloud anomaly detection aims to identify defective samples from the overall data distribution and further locate the abnormal regions deviating from the expected pattern in space. Existing global matching strategies struggle to effectively capture anomalies concentrated in local subtle geometric structures. To address this issue, a point cloud anomaly detection network based on local perception graph reconstruction is proposed. First, the point cloud is modeled as a graph structure, and local sensitive edge convolution operations are utilized to mine local structural features to enhance the identification ability for local subtle anomalies. Second, a local alignment reconstruction loss is designed based on a subgraph structure alignment strategy to amplify the differences between normal and abnormal samples at the structural level. Furthermore, a local anomaly simulation strategy is introduced to construct an anomaly-normal sample pair. Through this strategy, the model is constrained to learn the pattern mapping from abnormal samples to normal samples. Finally, a local matching algorithm is applied to calculate the differences between abnormal samples and expected normal samples to achieve the detection of point cloud abnormal regions. Experimental results show that the proposed method significantly enhances the perception ability for local details and achieves excellent performance on multiple categories of point cloud anomaly detection tasks.

Key words： Graph Neural Network Unsupervised Learning Anomaly Detection Point Cloud

Received: 20 October 2025

ZTFLH:

TP391

Fund:

Supported by Scientific and Technological Innovation 2030—"New Generation Artificial Intelligence" Major Project(No.2022ZD0117104), National Natural Science Foundation of China(No.62337001)

Corresponding Authors: HUANG Changqin, Ph.D., professor. His research inte-rests include machine learning, big data in education, and intelligent education.

About author:: GAO Chengling, Ph.D. candidate. Her research interests include graph representation learning and graph anomaly detection.
ZHENG Zhonglong, Ph.D., professor. His research interests include machine lear-ning, computer vision, artificial intelligence of things and blockchain.
JIANG Yunliang, Ph.D., professor. His research interests include intelligent information processing and geographic information systems.

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Cite this article:

GAO Chengling,HUANG Changqin,ZHENG Zhonglong等. Point Cloud Anomaly Detection Network Based on Local Perception Graph Reconstruction[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(11): 961-973.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202511001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I11/961

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