Looking 3D: Anomaly Detection with 2D-3D Alignment
<|reference_start|>Looking 3D: Anomaly Detection with 2D-3D Alignment: 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.<|reference_end|>
@article{bhunia2024looking,
    title={Looking 3D: Anomaly Detection with 2D-3D Alignment},
    author={Ankan Bhunia, Changjian Li, Hakan Bilen},
    journal={arXiv preprint arXiv:2406.19393},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19393},
    primaryClass={cs.CV}
}