Berkley Lab用Meta的SAM 3和DINOv3组合，把3D图像标注从一个月缩短到15分钟，效率提升近3000倍，搞科研的小伙伴可以关注。
SYNAPS-I项目由Berkeley Lab领导，使用Meta的SAM 3和DINOv3模型自动化图像分割。DINOv3提供全局语义和细粒度空间定位，SAM 3进行像素级边界提取。两者结合将3D体积标注所需时间从一个月（约43200分钟）压缩到约15分钟。该项目旨在加速科学发现并支持美国能源部的Genesis任务。
To accelerate scientific discovery and support @ENERGY’s Genesis Mission, the @BerkeleyLab-led SYNAP...
To accelerate scientific discovery and support @ENERGY ’s Genesis Mission, the @BerkeleyLab -led SYNAPS-I project is using SAM 3 and DINOv3 to automate image segmentation. By pairing DINOv3’s global semantic context and fine-grained spatial localization with SAM 3’s pixel-level boundary extraction, the researchers are able to compress 3D volume labeling from a month of manual effort to ~15 minutes. Learn more about their work: go.meta.me/01aed3 💬 4 🔄 2 ❤️ 18 👀 3573 📊 6 ⚡