Detecting and localizing defects in 3D point clouds is challenging because abnormal samples are scarce and diverse, while training is often limited to normal data. We propose Anomaly Factory 3D (AF3AD), a modular framework that synthesizes diverse pseudo-anomalies from normal point clouds to expand the training data for unsupervised 3D anomaly detection methods that rely on pseudo-anomalies.
AT3D-AD introduces a unified framework for detecting, localizing, and classifying 3D point‑cloud anomalies. It uses a Physics‑Driven Parametric Anomaly Synthesis module to generate synthetic defects for explicit supervision, a Hierarchical Global‑Local Anomaly Alignment module to refine representations, and a Semantic‑Geometric Anomaly Classification module to achieve precise, type‑discriminative localization. The method sets new state‑of‑the‑art results on four benchmarks, achieving high AUROC and Macro‑F1 scores.
By Jingyu Zeng, Haoquan Lu, Can Gao
arXiv:2609.14722v1 Announce Type: new
Abstract: Low-cost and low-resolution sensors used in edge deployments can produce test point clouds that are substantially sparser than the normal training data...
By Yutong Gu, Yingxi Xie, Kejin Huang, Jian Ning, Hanzhe Liang, Linlin Shen, Jinbao Wang
arXiv:2608.22789v1 Announce Type: new
Abstract: Additive Manufacturing (AM) plays a vital role in the ongoing industrial revolution. However, quality control remains crucial and challenging due to pr...
By Sosmita Paul, Krishna Roy
The paper introduces a training‑free anomaly detector that simultaneously handles structural and logical defects by calibrating heterogeneous anomaly cues with statistics from normal images. This calibration aligns frozen representations, allowing their fusion without extra training or part‑level supervision. The resulting method achieves state‑of‑the‑art AUROC scores on MVTec‑LOCO and remains competitive on MVTec‑AD.
By Changyi Li, Miao Yu, Kai Dong, Yu Xiao
The paper challenges the common assumption that RGB and point cloud data contribute equally to zero‑shot multimodal anomaly detection. It shows that point clouds are more reliable under category shift and introduces WOOPS, a framework that enhances point cloud features with a Multi‑view Information Decoupling module and calibrates modality contributions via a Modality Reliability Calibration module. Experiments demonstrate that WOOPS achieves top performance on new stringent metrics in both unimodal and multimodal settings, and that point cloud information also benefits RGB‑only inference.
By Chenglin Ye, Lupeng Liu, Dongbo Yu, Jun Xiao, Yunbiao Wang
arXiv:2609.37314v1 Announce Type: new
Abstract: Synthetic anomaly generation helps expand industrial anomaly datasets when real defects are scarce or unavailable. Existing approaches lie at two extre...
By Abhay Kumar Das, Rajesh Gangireddy, Ashwin Vaidya, Samet Akcay
GeoMAD is a multi‑view anomaly detection framework that fuses multiple camera viewpoints while maintaining geometric awareness and scalability to multi‑class industrial settings. It introduces a Cross‑view Deformable Fusion Module (CDFM) that learns view‑pair‑specific sampling offsets on 2D feature maps, enabling hierarchical cross‑view correspondence without camera calibration or voxel construction. Additionally, Distributional View Alignment (DVA) provides a self‑supervised loss that aligns bottleneck distributions across views, ensuring global consistency without pixel‑level correspondence. Together, CDFM and DVA achieve geometry‑aware, distribution‑consistent fusion and demonstrate strong detection and localization performance on Real‑IAD and MANTA‑Tiny datasets.
By Shang-Fu Chen, Jhih-Ciang Wu, Kuan-Chuan Peng, Wen-Huang Cheng, Kai-Lung Hua
arXiv:2607. 18142v1 Announce Type: cross Abstract: Industrial Video Anomaly Detection (IVAD) aims to identify anomalous objects and events in an industrial process, which is crucial for modern manufacturing and quality control systems.
By Mei Yuan, Qi Long, Qifeng Wu, Zhenyang Li, Yizhou Zhao, Lei Wang, Yang Liu, Min Xu
arXiv:2609.13282v1 Announce Type: new
Abstract: Industrial anomaly localization has advanced rapidly with feature-based, reconstruction-based, and distillation-based methods. Most of these methods sc...
By Yefan Wang
The paper introduces NC‑TFAD, a task‑free continual anomaly detection framework that leverages neural‑collapse geometry to learn from non‑stationary data streams without task boundaries. It freezes a pretrained backbone, aligns streaming features to a simplex Equiangular Tight Frame prototype space, and uses synthetic anomaly anchors, inter‑ and intra‑class regularization, and a Focal Neural Collapse Contrastive loss to stabilize representations and enhance normal‑anomaly separability. A normal‑patch‑prototype‑guided localization branch generates calibrated anomaly heatmaps, and extensive experiments on MVTec AD and VisA demonstrate that NC‑TFAD outperforms existing task‑free continual learning and unified anomaly detection baselines in both image‑level detection and pixel‑level localization.
By Xiaotong Kong, Chaoyang Song, Ziai Zhou, Jinxia Zhang, Kanjian Zhang, Haikun Wei
arXiv:2606. 15280v1 Announce Type: new Abstract: Most existing anomaly detection methods rely on estimating a probability density or learning an enclosing decision boundary, implicitly assuming that normal data occupies a region of non-zero volume in the ambient space.
By Alexander Bauer