PADFormer: Pose-agnostic Anomaly Detection from Sparse View Images
arXiv:2608.04210v1 Announce Type: new Abstract: Pose-agnostic Anomaly Detection (PAD) remains challenging as anomalies can appear under arbitrary viewpoints, requiring methods to handle significant pose variations. Existing approaches rely on complex 3D reconstruction, which are computationally expensive and require extensive multi-view data. We propose PADFormer, a novel image-space approach that leverages Vision Transformer (ViT) to directly reconstruct anomaly-free versions of query images while preserving pose information. Our key insight is to adapt cross-view masked reconstruction for anomaly detection through training exclusively on normal data, combined with dynamic patch selection and spatial alignment mechanisms that enable effective learning from sparse reference views under significant pose variations. During inference, we perform multiple forward passes with different masking patterns to generate an ensemble of anomaly-free reconstructions, ensuring comprehensive coverage of the query image. Anomalies are detected by comparing these reconstructions with the query image. PADFormer achieves state-of-the-art results on the PAD benchmark while maintaining comparable performance on classic few-shot anomaly detection (FSAD) tasks, demonstrating superior efficiency and generalization without requiring 3D reconstruction.
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[Submitted on 4 Aug 2026]
Title:PADFormer: Pose-agnostic Anomaly Detection from Sparse View Images
View a PDF of the paper titled PADFormer: Pose-agnostic Anomaly Detection from Sparse View Images, by Ruiqi Wang and 6 other authors
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Abstract:Pose-agnostic Anomaly Detection (PAD) remains challenging as anomalies can appear under arbitrary viewpoints, requiring methods to handle significant pose variations. Existing approaches rely on complex 3D reconstruction, which are computationally expensive and require extensive multi-view data. We propose PADFormer, a novel image-space approach that leverages Vision Transformer (ViT) to directly reconstruct anomaly-free versions of query images while preserving pose information. Our key insight is to adapt cross-view masked reconstruction for anomaly detection through training exclusively on normal data, combined with dynamic patch selection and spatial alignment mechanisms that enable effective learning from sparse reference views under significant pose variations. During inference, we perform multiple forward passes with different masking patterns to generate an ensemble of anomaly-free reconstructions, ensuring comprehensive coverage of the query image. Anomalies are detected by comparing these reconstructions with the query image. PADFormer achieves state-of-the-art results on the PAD benchmark while maintaining comparable performance on classic few-shot anomaly detection (FSAD) tasks, demonstrating superior efficiency and generalization without requiring 3D reconstruction.
Comments: Accepted to ECCV 2026 (oral)
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.04210 [cs.CV]
(or arXiv:2608.04210v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.04210
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ruiqi Wang [view email] [v1] Tue, 4 Aug 2026 20:23:34 UTC (20,843 KB)
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