DiffGlue: Diffusion-Aided Image Feature Matching

As one of the most fundamental computer vision problems, image feature matching aims to establish correct correspondences between two-view images. Existing studies enhance the descriptions of feature points with graph neural network (GNN), identifying correspondences with the predicted assignment matrix. However, this pipeline easily falls into a suboptimal result during training for the solution space is extremely complex, and is inaccessible to the prior that can guide the information propagation and network convergence. In this paper, we propose a novel method called DiffGlue that introduces the Diffusion Model into the sparse image feature matching framework. Concretely, based on the incrementally iterative diffusion and denoising processes, DiffGlue can be guided by the prior from the Diffusion Model and trained step by step on the optimization path, approaching the optimal solution progressively. Besides, it contains a special Assignment-Guided Attention as a bridge to merge the Diffusion Model and sparse image feature matching, which injects the inherent prior into GNN thereby ameliorating the message delivery. Extensive experiments reveal that DiffGlue converges faster and better, outperforming state-of-the-arts on several applications such as homography estimation, relative pose estimation, and visual localization. The code is available at https://github.com/SuhZhang/DiffGlue.