Grid-Guided Sparse Laplacian Consensus for Robust Feature Matching

Feature matching is a fundamental concern widely employed in computer vision applications. This paper introduces a novel and efficacious method named Grid-guided Sparse Laplacian Consensus, rooted in the concept of smooth constraints. To address challenging scenes such as severe deformation and independent motions, we devise grid-based adaptive matching guidance to construct multiple transformations based on motion coherence. Specifically, we obtain a set of precise yet sparse seed correspondences through motion statistics, facilitating the generation of an adaptive number of candidate correspondence sets. In addition, we propose an innovative formulation grounded in graph Laplacian for correspondence pruning, wherein mapping function estimation is formulated as a Bayesian model. We solve this utilizing EM algorithm with seed correspondences as initialization for optimal convergence. Sparse approximation is leveraged to reduce the time-space burden. A comprehensive set of experiments are conducted to demonstrate the superiority of our method over other state-of-the-art methods in both robustness to serious deformations and generalizability for various descriptors, as well as generalizability to multi motions. Additionally, experiments in geometric estimation, image registration, loop closure detection, and visual localization highlight the significance of our method across diverse scenes for high-level tasks.