PCAD: A Real-World Dataset for 6D Pose Industrial Anomaly Detection

Robert Maack, Lars Thun, Thomas Liang, Hasan Tercan, Tobias Meisen; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1132-1141

In industrial assembly line manufacturing visual inspection is crucial for maintaining high-quality standards. The detection of defects can be accomplished using anomaly detection by identifying irregularities in RGB images. However currently available datasets mostly contain only pose-invariant samples. Lately this has resulted in the emergence of datasets that supplement depth information into RGB image data enabling defect detection with respect to the geometrical surface of objects. Nevertheless the acquisition costs for high-resolution 3D point cloud data are considerably higher than the costs of conventional RGB cameras. Other datasets contain only synthetic 3D representations of the objects for model training. However this results in a considerable gap in the applicability of real-world industrial applications. Conversely multi-component CAD models are already an abundantly available source of 3D information yet not fully leveraged for anomaly detection. In this paper we introduce the dataset PCAD -- a dataset acquired using a specialized hardware setup designed to capture high-quality images of assembly groups with varying complexity and different poses in a systematic and reproducible way. The dataset includes variations in structural and semantic anomalies poses and lighting conditions providing a comprehensive real-world scenario for defect detection. We show the usability and efficacy of our dataset regarding various state-of-the-art anomaly detection models. Furthermore we demonstrate its applicability to visual quality inspection and its potential to support future research in this field. The code and dataset are publicly available: https://github.com/anonymous-conference5WaDb3Zx/pcad-dataset.git