Unsupervised pose-agnostic visual anomaly detection in realistic industrial scenes

Recent works on pose-agnostic anomaly detection (PAD) have addressed the challenge of identifying visual defects when the test object’s pose is unknown, that is, when test images may depict the same object but in arbitrary orientations not seen in the reference anomaly-free dataset. In this unsupervised setting, models rely only on the knowledge of non-defective samples and their task is to detect anomalies appearing anywhere on the object surface. Current state-of-the-art approaches, such as OmniPoseAD, SplatPose, and SplatPose+, have advanced the field by introducing dedicated algorithms and frameworks for pose-agnostic anomaly detection. The present work consists of an engineering-oriented integration effort aimed at adapting existing PAD approaches to realistic industrial scenarios in which background clutter must be addressed for practical deployment. Two main contributions are provided: first, a simulated dataset for pose-agnostic anomaly detection with realistic industrial scenes; second, a complete pipeline that handles the introduced scenarios. Experimental results, carried out in comparison with the state-of-the-art SplatPose+ and measured in terms of pixel-level AUROC, AUPRO, image-level AUROC, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi>F</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -score, demonstrate good performance on the proposed dataset. Code is available at: https://github.com/enmarchi/3dpad_background .