VGGSfM is a depth learning-driven 3D reconstruction technology aimed at reconstructing the camera pose and 3D structure of a scene from a set of unconstrained 2D images. This technology uses a fully differentiable deep learning framework for end-to-end training. It extracts reliable pixel-level trajectories using depth 2D point tracking technology, while restoring all cameras based on image and trajectory features, and optimizing camera and triangulated 3D points through a differentiable bundle adjustment layer. VGGSfM achieves state-of-the-art performance in three popular datasets: CO3D, IMC Phototourism, and ETH3D.