Self-supervised point cloud anomaly detection method based on point cloud reconstruction

doi:10.11772/j.issn.1001-9081.2024091347

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (10): 3302-3310.DOI: 10.11772/j.issn.1001-9081.2024091347

• Multimedia computing and computer simulation • Previous Articles

Self-supervised point cloud anomaly detection method based on point cloud reconstruction

Jianfeng YANG¹^,², Bin CHEN²^,³^,⁴(), Yuxuan LI¹^,²

^1.Chengdu Institute of Computer Application，Chinese Academy of Sciences，Chengdu Sichuan 610213，China
^2.University of Chinese Academy of Sciences，Beijing 100049，China
^3.International Institute for Artificial Intelligence，Harbin Institute of Technology （Shenzhen），Shenzhen Guangdong 518055，China
^4.Chongqing Research Institute，Harbin Institute of Technology，Chongqing 401151，China

Received:2024-09-23 Revised:2024-12-21 Accepted:2024-12-23 Online:2025-01-14 Published:2025-10-10
Contact: Bin CHEN
About author:YANG Jianfeng， born in 1998， M. S. candidate. His research interests include machine vision， anomaly detection.
CHEN Bin， born in 1970， Ph. D.， research fellow. His research interests include industrial detection， deep learning.
LI Yuxuan， born in 1999， M. S. candidate. His research interests include anomaly detection， deep learning.

基于点云重构的自监督点云异常检测方法

杨建锋¹^,², 陈斌²^,³^,⁴(), 李雨轩¹^,²

^1.中国科学院成都计算机应用研究所，成都 610213
^2.中国科学院大学，北京 100049
^3.哈尔滨工业大学（深圳）国际人工智能研究院，广东深圳 518055
^4.哈尔滨工业大学重庆研究院，重庆 401151

通讯作者: 陈斌
作者简介:杨建锋（1998—），男，江西南昌人，硕士研究生，主要研究方向：机器视觉、异常检测
陈斌（1970—），男，四川广汉人，研究员，博士，CCF会员，主要研究方向：工业检测、深度学习 Email:chenbin2020@hit.edu.cn
李雨轩（1999—），男，河南商丘人，硕士研究生，主要研究方向：异常检测、深度学习。

Abstract

Abstract:

As industrial production environments become more and more complex， demand for three-dimensional point cloud industrial anomaly detection is increasing. Although the two-dimensional anomaly detection methods based on pre-trained network have significant effects， generalization ability of the three-dimensional point cloud pre-training network is limited， which leads to poor effect of this kind of point cloud anomaly detection methods. To improve performance of the three-dimensional point cloud anomaly detection， Point-ReAD， an anomaly detection method based on point cloud reconstruction， was proposed. The proposed method consists of an anomaly simulation module， a point cloud reconstruction network， and an anomaly discrimination module. In specific， during training phase， anomalous point clouds were created from normal point cloud maps by the anomaly simulation module， with the normal point clouds served as self-supervised signals to guide the learning process of the reconstruction network； in the point cloud reconstruction network， Group Attention Module （GAM） was used to design complex structural information for point cloud integration， thereby capturing geometric and semantic features in point clouds effectively； in the inference phase， the tested point clouds were input to the reconstruction network to generate reconstructed point clouds， and anomalies were located accurately through the anomaly discrimination module by comparing the point clouds before and after reconstruction. Experimental results show that Point-ReAD achieves the PC-AUROC （PointCloud level Area Under the Receiver Operator characteristic Curve） and the point-level AUPRO （Area Under the Per-Region Overlap） of 95.49% and 94.66%， respectively， on MVTec 3D-AD dataset， which are improved by 0.89， 1.27 percentage points， compared to subprior method 3DR?M （3D Discriminatively trained Reconstruction Anomaly Embedding Model）.

Key words: anomaly detection, self-supervised learning, point cloud, computer vision, reconstruction network

摘要：

面对日趋复杂的工业生产环境，三维点云工业异常检测需求与日俱增。尽管基于预训练网络的二维异常检测方法效果显著，但三维点云预训练网络的泛化能力有限，导致这类点云异常检测方法的效果不佳。为提高三维点云异常检测的性能，提出一种基于点云重构的异常检测方法Point-ReAD（Point cloud Reconstruction for Anomaly Detection），它由异常模拟模块、点云重构网络和异常判别模块这3个核心模块构成。在训练阶段，正常点云图经异常模拟模块产生异常点云送入点云重构网络，正常点云作为自监督信号指导重构网络的学习；点云重构网络使用分组注意力模块（GAM），用于融合点云的复杂结构信息，从而有效地捕捉点云中的几何和语义特征。在推理阶段，测试点云进入重构网络后生成重构点云，使用异常判别模块比较重构前后的点云，从而精确定位异常。实验结果表明，Point-ReAD在MVTec 3D-AD数据集上的点云级AUROC（PC-AUROC）和点级AUPRO（Area Under the Per-Region Overlap）分别达到了95.49%和94.66%，相较于次优方法3DR?M（3D Discriminatively trained Reconstruction Anomaly Embedding Model）分别提升了0.89和1.27个百分点。

关键词: 异常检测, 自监督学习, 点云, 计算机视觉, 重构网络

CLC Number:

TP391.4

Jianfeng YANG, Bin CHEN, Yuxuan LI. Self-supervised point cloud anomaly detection method based on point cloud reconstruction[J]. Journal of Computer Applications, 2025, 45(10): 3302-3310.

杨建锋, 陈斌, 李雨轩. 基于点云重构的自监督点云异常检测方法[J]. 《计算机应用》唯一官方网站, 2025, 45(10): 3302-3310.

Figures/Tables 15

References 28

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方法	bagel	cable_gland	carrot	cookie	dowel	foam	peach	potato	rope	tire	mean
Voxel AE^［2］	69.30	42.50	51.50	79.00	49.40	55.80	53.70	48.40	63.90	58.30	57.18
PatchCore^［5］	62.40	68.30	67.60	83.80	60.80	55.80	56.70	49.60	69.90	61.90	57.18
PC-FPFH^［6］	82.00	53.30	87.70	76.90	71.80	57.40	77.40	89.50	99.00	58.20	75.32
3D-ST^［9］	86.20	48.40	83.20	89.40	84.80	66.30	76.30	68.70	95.80	48.60	74.77
M3DM^［7］	94.10	65.10	96.50	96.90	90.50	76.00	88.00	97.40	92.60	76.50	87.36
AST^［28］	88.10	57.60	96.50	95.70	67.90	79.70	99.00	91.50	95.60	61.10	83.27
3DRÆM^［20］	98.90	73.10	99.60	98.60	98.80	86.60	96.70	96.90	97.70	99.10	94.60
Point-ReAD	98.44	87.07	98.11	97.74	96.90	87.81	96.27	97.14	98.38	96.99	95.49

方法	bagel	cable_gland	carrot	cookie	dowel	foam	peach	potato	rope	tire	mean
Voxel AE^［2］	69.30	42.50	51.50	79.00	49.40	55.80	53.70	48.40	63.90	58.30	57.18
PatchCore^［5］	62.40	68.30	67.60	83.80	60.80	55.80	56.70	49.60	69.90	61.90	57.18
PC-FPFH^［6］	82.00	53.30	87.70	76.90	71.80	57.40	77.40	89.50	99.00	58.20	75.32
3D-ST^［9］	86.20	48.40	83.20	89.40	84.80	66.30	76.30	68.70	95.80	48.60	74.77
M3DM^［7］	94.10	65.10	96.50	96.90	90.50	76.00	88.00	97.40	92.60	76.50	87.36
AST^［28］	88.10	57.60	96.50	95.70	67.90	79.70	99.00	91.50	95.60	61.10	83.27
3DRÆM^［20］	98.90	73.10	99.60	98.60	98.80	86.60	96.70	96.90	97.70	99.10	94.60
Point-ReAD	98.44	87.07	98.11	97.74	96.90	87.81	96.27	97.14	98.38	96.99	95.49

类别	bagel	cable_gland	carrot	cookie	dowel	foam	peach	potato	rope	tire	mean
Voxel AE^［2］	26.00	34.10	58.10	35.10	50.20	23.40	35.10	65.80	1.50	18.50	34.78
PatchCore^［5］	70.10	54.40	79.10	83.50	53.10	10.00	80.00	54.90	82.70	18.50	58.63
PC-FPFH^［6］	97.20	84.90	98.10	93.60	96.30	69.30	97.50	98.10	98.00	94.90	92.79
3D-ST^［9］	95.00	48.30	98.60	92.10	90.50	63.20	94.50	98.80	97.60	54.20	83.28
M3DM^［7］	94.30	81.80	97.70	88.20	88.10	74.30	95.80	97.40	95.00	92.90	90.55
3DRÆM^［20］	97.60	87.90	98.30	94.60	94.80	81.20	97.90	98.40	85.30	97.90	93.39
Point-ReAD	97.75	92.02	97.77	94.89	95.11	78.89	96.88	98.52	98.80	95.96	94.66

类别	bagel	cable_gland	carrot	cookie	dowel	foam	peach	potato	rope	tire	mean
Voxel AE^［2］	26.00	34.10	58.10	35.10	50.20	23.40	35.10	65.80	1.50	18.50	34.78
PatchCore^［5］	70.10	54.40	79.10	83.50	53.10	10.00	80.00	54.90	82.70	18.50	58.63
PC-FPFH^［6］	97.20	84.90	98.10	93.60	96.30	69.30	97.50	98.10	98.00	94.90	92.79
3D-ST^［9］	95.00	48.30	98.60	92.10	90.50	63.20	94.50	98.80	97.60	54.20	83.28
M3DM^［7］	94.30	81.80	97.70	88.20	88.10	74.30	95.80	97.40	95.00	92.90	90.55
3DRÆM^［20］	97.60	87.90	98.30	94.60	94.80	81.20	97.90	98.40	85.30	97.90	93.39
Point-ReAD	97.75	92.02	97.77	94.89	95.11	78.89	96.88	98.52	98.80	95.96	94.66

方法	bagel		cable_gland		carrot		cookie		dowel		foam
方法	PC-AUROC	AUPRO	PC-AUROC	AUPRO	PC-AUROC	AUPRO	PC-AUROC	AUPRO	PC-AUROC	AUPRO	PC-AUROC	AUPRO
Mask	94.67	97.67	79.50	90.33	89.02	97.32	96.67	93.52	92.26	91.56	84.96	75.40
Point-ReAD	98.44	97.75	87.07	92.02	98.11	97.77	97.74	94.89	96.90	95.11	87.82	78.89
方法	peach		potato		rope		tire		mean
方法	PC-AUROC	AUPRO	PC-AUROC	AUPRO	PC-AUROC	AUPRO	PC-AUROC	AUPRO	PC-AUROC	AUPRO
Mask	93.85	95.69	97.23	95.67	94.75	97.55	92.37	94.78	91.53	92.95
Point-ReAD	96.27	96.88	97.14	98.52	98.38	98.80	96.99	95.96	95.49	94.66

Self-supervised point cloud anomaly detection method based on point cloud reconstruction

基于点云重构的自监督点云异常检测方法

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Figures/Tables 15

References 28

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Metrics

注意力模块	无仿射变换		有仿射变换
注意力模块	PC-AUROC	AUPRO	PC-AUROC	AUPRO
标准	89.23	93.84	92.39	94.08
GAM	93.30	94.39	95.49	94.66

策略	重构局部	重构全局	重构整体	PC-AUROC	AUPRO
I	√	√		95.49	94.66
Ⅱ	√			89.31	92.29
Ⅲ		√		79.62	84.12
Ⅳ			√	92.04	93.03

F	PC-AUROC/%
F	I=0.2	I=0.3	I=0.4	I=0.5	I=0.6
0.2	93.05	94.31	91.52	86.30	77.95
0.4	94.88	95.49	94.30	88.07	76.55
0.6	90.43	91.14	86.01	81.33	/
0.8	73.23	75.28	74.50	/	/

方法	bagel	cable_gland	carrot	cookie	dowel	foam	peach	potato	rope	tire	mean
AST_RGB	94.70	92.80	85.10	82.50	98.10	95.10	89.50	61.30	99.20	82.10	88.00
M3DM_RGB	94.40	91.80	89.60	74.90	95.90	76.70	91.90	64.80	93.80	76.70	85.05
3DRÆM_RGB	94.20	90.60	81.60	66.60	78.30	91.90	65.60	78.40	98.60	92.50	83.83
Point-ReAD	98.44	87.07	98.11	97.74	96.90	87.81	96.27	97.14	98.38	96.99	95.49

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