DINOv3 is Meta AI's third generation of self-supervised vision foundation models, featuring a substantial 7-billion parameter Vision Transformer trained on 1.7 billion unlabeled images. It stands out for its scale, stability, and versatility, enabling high-quality global and dense features applicable to various tasks such as image classification, semantic segmentation, depth estimation, and object tracking. The model's innovative Gram Anchoring technique stabilizes dense features during training, addressing previous issues of feature degradation and improving performance on dense prediction tasks. As a universal frozen backbone, DINOv3 allows for efficient post-hoc adaptation across diverse domains, reducing the need for large annotated datasets and retraining while maintaining strong performance on benchmarks like ImageNet. Real-world applications include measuring tree canopy heights from satellite imagery and aiding Mars exploration robots, showcasing its adaptability to domains with limited labels and resource constraints. Meta has made DINOv3 openly available, providing pretrained weights and documentation to the research community, although challenges such as domain sensitivity and annotation propagation drift remain.