#Meta AI Research

DINOv2 is designed for robust visual feature extraction using unsupervised learning on a dataset of 142 million images. Its features work effortlessly with simple classifiers like linear layers, performing well in diverse computer vision tasks without the need for fine-tuning. With its integration of registers in Vision Transformers, DINOv2 offers improved performance, showcasing the latest advancements in the field. Available in multiple configurations via PyTorch Hub, it supports applications in image classification, depth estimation, and semantic segmentation. Discover how DINOv2's pretrained models enhance visual feature robustness and versatility.

Discover cutting-edge 3D reconstruction strategies using VGGSfM, which combines deep learning for structure-from-motion with dynamic sequence analysis. The project facilitates dense point cloud generation and camera pose recovery from sequential data frames. Recent enhancements include functionalities like dense depth map export and Gaussian splatting model training. Optimized for processing over 1,000 video frames and adept at filtering dynamic objects via masks, VGGSfM serves as a precise tool for practitioners and developers in the fields of computer vision and augmented reality.

Faiss is a C++ library by Meta's AI Research team for similarity search and clustering of dense vectors. It supports Python wrappers and utilizes GPU for faster processing, handling billions of vectors. Faiss includes L2, dot product, and cosine similarity algorithms, with indexing structures like HNSW and NSG for efficiency. Compatible with CPU and GPU, it integrates easily with Anaconda, making it essential for managing high-dimensional vector data.