  In this paper we tackle the problem of learning Structure-from-Motion (SfM)
through the use of graph attention networks. SfM is a classic computer vision
problem that is solved though iterative minimization of reprojection errors,
referred to as Bundle Adjustment (BA), starting from a good initialization. In
order to obtain a good enough initialization to BA, conventional methods rely
on a sequence of sub-problems (such as pairwise pose estimation, pose averaging
or triangulation) which provide an initial solution that can then be refined
using BA. In this work we replace these sub-problems by learning a model that
takes as input the 2D keypoints detected across multiple views, and outputs the
corresponding camera poses and 3D keypoint coordinates. Our model takes
advantage of graph neural networks to learn SfM-specific primitives, and we
show that it can be used for fast inference of the reconstruction for new and
unseen sequences. The experimental results show that the proposed model
outperforms competing learning-based methods, and challenges COLMAP while
having lower runtime. Our code is available at
https://github.com/lucasbrynte/gasfm/.
