Looking 3D: Anomaly Detection with 2D-3D Alignment

• URL: http://arxiv.org/abs/2406.19393v1
• Date: Thu, 27 Jun 2024 17:59:46 GMT
• Title: Looking 3D: Anomaly Detection with 2D-3D Alignment
• Authors: Ankan Bhunia, Changjian Li, Hakan Bilen,
• Abstract summary: This paper introduces a new conditional anomaly detection problem, which involves identifying anomalies in a query image by comparing it to a reference shape. We have created a large dataset, BrokenChairs-180K, consisting of around 180K images, with diverse anomalies, geometries, and textures paired with 8,143 reference 3D shapes. Our approach has been rigorously evaluated through comprehensive experiments, serving as a benchmark for future research in this domain.
• Score: 27.474201071615187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic anomaly detection based on visual cues holds practical significance in various domains, such as manufacturing and product quality assessment. This paper introduces a new conditional anomaly detection problem, which involves identifying anomalies in a query image by comparing it to a reference shape. To address this challenge, we have created a large dataset, BrokenChairs-180K, consisting of around 180K images, with diverse anomalies, geometries, and textures paired with 8,143 reference 3D shapes. To tackle this task, we have proposed a novel transformer-based approach that explicitly learns the correspondence between the query image and reference 3D shape via feature alignment and leverages a customized attention mechanism for anomaly detection. Our approach has been rigorously evaluated through comprehensive experiments, serving as a benchmark for future research in this domain.

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