Learning Structure-From-Motion with Graph Attention Networks

Abstract

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 initial-ization 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 re-place 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 key-point coordinates. Our model takes advantage of graph neural networks to learn SfM-specijic 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 com-peting learning-based methods, and challenges COLMAP while having lower runtime. Our code is available at: https://github.com/lucasbrynte/gasfm/.