SCUGSC: Robust Model Fitting Via Spatial Consensus Unit Guided Sampling Consensus for Feature Matching

Robust model fitting serves as a critical component in artificial intelligence and computer vision, extensively employed for geometric model estimation from image feature correspondences. Sampling strategy is key to robust model fitting. However, traditional algorithms like RANSAC struggle to sample all-inlier minimal subsets under high outlier ratios or multi-structure scenarios. To address this issue, a robust sampling algorithm for model fitting, termed SCUGSC, is proposed in this paper. This algorithm constructs Spatial Consensus Units (SCUs) to capture local correlations and geometric consistency, which then guide data subset selection. Candidate points with the highest consistency are selected as initial subset elements and expanded via compatibility scores. A subset refinement strategy and an SCU-based stopping criterion further enhance estimation accuracy and convergence stability under low inlier ratios. Experiments on five challenging datasets demonstrate that SCUGSC significantly outperforms seven state-of-the-art methods. SUCGSC achieves the lowest segmentation error of 0.50%, with 98.97% F-score and 98.21% precision on CIAP. Across all datasets, SCUGSC achieves an average F-score of 93.49%, substantially outperforming RANSAC, while maintaining competitive computational efficiency with runtime ranging from 0.10s to 0.25s. These results validate the effectiveness and robustness of SCUGSC in high-outlier-ratio and multi-structure scenarios.