In recent years, 3D point clouds (PCs) have gained significant attention due to their diverse applications across various fields, such as computer vision (CV), condition monitoring (CM), virtual reality, robotics, autonomous driving, etc. Deep learning (DL) has proven effective in leveraging 3D PCs to address various challenges encountered in 2D vision. However, applying deep neural networks (DNNs) to process 3D PCs presents unique challenges. This paper provides an in-depth review of recent advancements in DL-based industrial CM using 3D PCs, with a specific focus on defect shape classification and segmentation within industrial applications. Recognizing the crucial role of these aspects in industrial maintenance, the paper offers insightful observations on the strengths and limitations of the reviewed DL-based PC processing methods. This knowledge synthesis aims to contribute to understanding and enhancing CM processes, particularly within the framework of remaining useful life (RUL), in industrial systems.
Baylor University（贝勒大学）；University of Connecticut（康涅狄格大学）；University of Michigan（密歇根大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；New York University（纽约大学）；University of Southern California（南加利福尼亚大学）
IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detection
Software College, Northeastern University（东北大学软件学院）；Shenzhen Audencia Financial Technology Institute, Shenzhen University（深圳Audencia金融技术研究院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）
A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data
College of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；Institute of Physical Education (Main Campus), Zhengzhou University（郑州大学体育学院（主校区））
A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology（智能制造装备与技术国家重点实验室，华中科技大学）；School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；Department of Precision Instrument, Tsinghua University（精密仪器系，清华大学）；Geely Automobile Research Institute (Ningbo) Co., Ltd.（吉利汽车研究院（宁波）有限公司）
CF-YOLO: Context-Aware Feature Refinement for Camouflaged Industrial Micro-Defect Detection
School of Information Engineering, Huzhou University（湖州大学信息工程学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG19
Unsupervised Learning for Industrial Defect Detection: A Case Study on Shearographic Data
Trier, University of Applied Science（特里尔应用科学大学）；RPTU University Kaiserslautern-Landau（科隆-兰道大学）；Department of Physics（物理系）；research center OPTIMAS（OPTIMAS研究中心）；University of Luxembourg（卢森堡大学）；Fraunhofer Institute for Industrial Mathematics ITWM（弗劳恩霍夫工业数学研究所ITWM）
Real-Time Industrial Defect Detection on Edge Hardware Using Fine-Tuned YOLOv8: A Systematic Benchmark on the NEU Surface Defect Database and MVTec AD with Automotive & Battery Manufacturing Extensions
Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Binzhou Institute of Technology（滨州理工学院）；Institute of Automation, Gansu Academy of Sciences（甘肃省科学院自动化研究所）
Zero-Shot Multi-Criteria Visual Quality Inspection for Semi-Controlled Industrial Environments via Real-Time 3D Digital Twin Simulation
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；Costa Rica Institute of Technology（哥斯达黎加理工学院）；Gestalt Automation GmbH（Gestalt Automation有限公司）；Technical University Berlin（柏林工业大学）
Resilient Multimodal Industrial Surface Defect Detection with Uncertain Sensors Availability
School of Artificial Intelligence and Robotics（人工智能与机器人学院）；National Engineering Research Center for Robot Visual Perception and Control Technology（机器人视觉感知与控制技术国家工程研究中心）；Hunan University（湖南大学）
Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation
Institute for Infocomm Research (I2R), the Agency for Science, Technology and Research (A*STAR)（资讯通信研究院（I2R），新加坡科技研究局（A*STAR））；Zhejiang University（浙江大学）
LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning
School of Computer Science and Technology, Guangdong University of Technology（广东技术大学计算机科学与技术学院）；School of Computer Science and Intelligence Education, Lingnan Normal University（岭南师范学院计算机科学与智能教育学院）
Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics
Hong Kong University of Science and Technology, Guangzhou（香港科技大学（广州））；HKUST(GZ) - SmartMore Joint Lab（香港科技大学（广州）- 赛摩智能联合实验室）；SmartMore. Corp（赛摩智能公司）；Hong Kong University of Science and Technology（香港科技大学）
Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection
Trier University of Applied Sciences（特里尔应用科学大学）；RPTU University Kaiserslautern-Landau（凯撒斯劳滕-兰道大学）；University of Luxembourg（卢森堡大学）；Fraunhofer Institute for Industrial Mathematics ITWM（弗劳恩霍夫工业数学研究所ITWM）
Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey
University of Chinese Academy of Sciences（中国科学院大学）；Beijing Technology and Business University（北京工商大学）；Indian Institute of Technology Patna（印度理工学院巴特那分校）
Adaptive Modular Retrieval-Augmented Framework for Cross-Domain Image Classification
Department of Mathematics, Indian Institute of Technology Kharagpur（数学系，印度理工学院Kharagpur分校）；Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur（计算机科学与工程系，印度理工学院Kharagpur分校）
Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study
Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco（佩南布科联邦大学信息学中心 Voxar 实验室）；Visual Computing Lab, DC, Universidade Federal Rural de Pernambuco（佩南布科联邦农村大学 DC 视觉计算实验室）；Chemnitz University of Technology（开姆尼茨工业大学）
ICME 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing
East China Normal University（华东师范大学）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Université de Nantes（南特大学）
