3D Anomaly

3D anomaly detection focuses on identifying deviations from normal patterns within three-dimensional data, such as point clouds or volumetric scans, primarily in industrial quality control and medical imaging. Current research emphasizes unsupervised and few-shot learning approaches, employing architectures like transformers, generative adversarial networks (GANs), and diffusion models, often incorporating multimodal data (RGB images and 3D scans) and leveraging techniques like feature contrastive learning and self-supervised feature adaptation to improve robustness and accuracy. This field is significant for its potential to automate defect detection in manufacturing, enhance medical diagnostics, and improve the efficiency and reliability of various inspection processes.