FastJAM: a Fast Joint Alignment Model for Images
Omri Hirsch, Ron Shapira Weber, Shira Ifergane, Oren Freifeld
Joint Alignment (JA) of images aims to align a collection of images into a unified coordinate frame, such that semantically similar features appear at corresponding spatial locations. Most existing approaches often require extensive training times, large-capacity models,and extensive hyperparameter tuning. We introduce FastJAM, a rapid, graph-based method that drastically reduces the computational complexity of joint alignment tasks. FastJAM leverages pairwise matches computed by off-the-shelf image matchers to construct a graph representing intra- and inter-image keypoint relations. A graph neural network propagates and aggregates these correspondences, efficiently predicting per-image homography parameters via image-level pooling. Utilizing an inverse-compositional warping strategy, FastJAM performs image JA quickly and effectively. Experimental results on several benchmarks demonstrate that FastJAM achieves results better than existing modern JA methods in terms of alignment quality, while reducing computation time from hours or minutes to mere seconds. Our code will be made public upon acceptance.