Contrastive Learning on 3D Point Clouds for Robotic Geometric Defect Detection

Abstract

Robotic quality inspection is emerging as a key enabler in intelligent manufacturing, allowing robots to transcend human limitations in endurance, consistency, and access to complex structures. By detecting subtle defects with speed and precision, robotic inspection enhances efficiency while elevating production quality. While most existing approaches emphasize 2D image-based surface defect detection, they often overlook geometric defects, which are more prevalent and challenging in industrial inspection. To overcome this gap, we formulate geometric defect detection as anomaly detection in 3D point clouds and propose a novel framework that integrates contrastive learning with spatially aware comparisons of local geometries. Specifically, we partition point cloud surfaces into patches and employ contrastive learning to train a neural network-based feature extractor capable of capturing rich geometric representations. An anomaly detection algorithm is then introduced to identify defects by comparing patch-level features in a spatially consistent manner. Evaluated on the recent Real3D-AD benchmark, our method achieves a mean area under the ROC curve of 0.901, establishing a new state of the art and demonstrating the potential of robotic inspection systems to move beyond human limitations in detecting subtle geometric anomalies