This academic paper introduces **DINOv3**, a significant advancement in **self-supervised learning (SSL)** for computer vision models. It highlights how **SSL enables training on vast raw image datasets**, leading to versatile and robust "foundation models" that generalize across diverse tasks without extensive fine-tuning. A key innovation is **Gram anchoring**, a novel training strategy that addresses the degradation of dense feature maps often seen in large-scale models, ensuring DINOv3 excels in both high-level semantic and precise geometric tasks. The paper also explores **architectural scaling to a 7-billion parameter model**, data curation techniques, and post-training stages like **resolution adaptation, model distillation**, and **text alignment**, showcasing DINOv3's superior performance across various benchmarks, including object detection, semantic segmentation, and even geospatial applications.