  Structure-from-motion (SfM) is a long-standing problem in the computer vision
community, which aims to reconstruct the camera poses and 3D structure of a
scene from a set of unconstrained 2D images. Classical frameworks solve this
problem in an incremental manner by detecting and matching keypoints,
registering images, triangulating 3D points, and conducting bundle adjustment.
Recent research efforts have predominantly revolved around harnessing the power
of deep learning techniques to enhance specific elements (e.g., keypoint
matching), but are still based on the original, non-differentiable pipeline.
Instead, we propose a new deep pipeline VGGSfM, where each component is fully
differentiable and thus can be trained in an end-to-end manner. To this end, we
introduce new mechanisms and simplifications. First, we build on recent
advances in deep 2D point tracking to extract reliable pixel-accurate tracks,
which eliminates the need for chaining pairwise matches. Furthermore, we
recover all cameras simultaneously based on the image and track features
instead of gradually registering cameras. Finally, we optimise the cameras and
triangulate 3D points via a differentiable bundle adjustment layer. We attain
state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism,
and ETH3D.
