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

Autor:	Bhunia, Ankan, Li, Changjian, Bilen, Hakan
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at CVPR'24. Codes & dataset available at https://github.com/VICO-UoE/Looking3D
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.19393 Zobrazit plný text záznamu View this record from Arxiv