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High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing.
A ransac-based approach to model fitting and its application to finding cylinders in range data
Robert C. Bolles and Martin A. Fischler · 1981
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A geometric framework for unsupervised anomaly detection
Eleazar Eskin, Andrew O. Arnold, Michael J. Prerau, Leonid Portnoy, and S. Stolfo · 2002
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Weakly supervised learning for industrial optical inspection
Matthias Wieler and Tobias Hahn · 2007
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Fast point feature histograms (fpfh) for 3d registration
Radu Bogdan Rusu, Nico Blodow, and Michael Beetz · 2009
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Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2014
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
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Cloudcompare - a 3d pointcloud and mesh software
CloudCompare Community · 2016
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Blender - a 3d modelling and rendering package
Blender Online Community · 2018
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Towards total recall in industrial anomaly detection
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler · 2021
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Point-bert: Pre-training 3d point cloud transformers with masked point modeling
Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, and Jiwen Lu · 2021
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Draem-a discriminatively trained reconstruction embedding for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj · 2021
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Anomaly detection in 3d point clouds using deep geometric descriptors
Paul Bergmann and David Sattlegger · 2022
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The eyecandies dataset for unsupervised multimodal anomaly detection and localization
Luca Bonfiglioli, Marco Toschi, Davide Silvestri, Nicola Fioraio, and Daniele De Gregorio · 2022
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Informative knowledge distillation for image anomaly segmentation
Yunkang Cao, Qian Wan, Weiming Shen, and Liang Gao · 2022
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A survey on unsupervised industrial anomaly detection algorithms
Yajie Cui, Zhaoxiang Liu, and Shiguo Lian · 2022
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Anomaly detection via reverse distillation from one-class embedding
Hanqiu Deng and Xingyu Li · 2022
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Softpatch: Unsupervised anomaly detection with noisy data
Xi Jiang, Jianlin Liu, Jinbao Wang, Qiang Nie, Kai Wu, Yong Liu, Chengjie Wang, and Feng Zheng · 2022
Cited alongside, same era.
Masked autoencoders for point cloud self-supervised learning
Yatian Pang, Wenxiao Wang, Francis E. H. Tay, W. Liu, Yonghong Tian, and Liuliang Yuan · 2022
Cited alongside, same era.
The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization
Collaborative discrepancy optimization for reliable image anomaly localization
Yunkang Cao, Xiaohao Xu, Zhaoge Liu, and Weiming Shen · 2023
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Complementary pseudo multimodal feature for point cloud anomaly detection
Yunkang Cao, Xiaohao Xu, and Weiming Shen · 2023
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Segment any anomaly without training via hybrid prompt regularization
Yunkang Cao, Xiaohao Xu, Chen Sun, Yuqi Cheng, Zongwei Du, Liang Gao, and Weiming Shen · 2023
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Easynet: An easy network for 3d industrial anomaly detection
Ruitao Chen, Guoyang Xie, Jiaqi Liu, Jinbao Wang, Ziqi Luo, Jinfan Wang, and Feng Zheng · 2023
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Back to the feature: classical 3d features are (almost) all you need for 3d anomaly detection
Eliahu Horwitz and Yedid Hoshen · 2023
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Bergmann Paul, Jin Xin, Sattlegger David, and Steger Carsten · 2022
Cited alongside, same era.
Towards total recall in industrial anomaly detection
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler · 2022
Cited alongside, same era.
Asymmetric student-teacher networks for industrial anomaly detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt · 2022
Cited alongside, same era.
Deep learning for unsupervised anomaly localization in industrial images: A survey
Xian Tao, Xinyi Gong, Xin Yu Zhang, Shaohua Yan, and Chandranath Adak · 2022
Cited alongside, same era.
Position encoding enhanced feature mapping for image anomaly detection
Qian Wan, Yunkang Cao, Liang Gao, Weiming Shen, and Xinyu Li · 2022
Cited alongside, same era.
Pushing the limits of fewshot anomaly detection in industry vision: Graphcore
Guoyang Xie, Jinbao Wang, Jiaqi Liu, Yaochu Jin, and Feng Zheng · 2022
Cited alongside, same era.
A unified model for multi-class anomaly detection
Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, and Xinyi Le · 2022
Cited alongside, same era.
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Deep industrial image anomaly detection: A survey
Jiaqi Liu, Guoyang Xie, Jingbao Wang, Shangnian Li, Chengjie Wang, Feng Zheng, and Yaochu Jin · 2023
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Revisiting reverse distillation for anomaly detection
Tran Dinh Tien, Anh Tuan Nguyen, Nguyen Hoang Tran, Ta Duc Huy, Soan Duong, Chanh D Tr Nguyen, and Steven QH Truong · 2023
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Multimodal industrial anomaly detection via hybrid fusion
Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang · 2023
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Im-iad: Industrial image anomaly detection benchmark in manufacturing
Guoyang Xie, Jinbao Wang, Jiaqi Liu, Jiayi Lyu, Yong Liu, Chengjie Wang, Feng Zheng, and Yaochu Jin · 2023
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Explicit boundary guided semi-push-pull contrastive learning for supervised anomaly detection
Xincheng Yao, Ruoqi Li, Jing Zhang, Jun Sun, and Chongyang Zhang · 2023
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What makes a good data augmentation for few-shot unsupervised image anomaly detection?
Lingrui Zhang, Shuheng Zhang, Guoyang Xie, Jiaqi Liu, Hua Yan, Jinbao Wang, Feng Zheng, and Yaochu Jin · 2023
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Omnial: A unified cnn framework for unsupervised anomaly localization
Ying Zhao · 2023
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