Abstract
Robust model fitting aims to estimate model parameters from data contaminated by noise and outliers in computer vision. Traditional RANSAC-based methods suffer from model hypothesis ambiguity and inefficiency due to the problems of neglecting data preference distributions and employing iterative hypothesis sampling. Learning-based methods enhance traditional methods through deep features. However, their reliance on static coordinate representations inherently lacks motion cues, hindering the analysis of complex dynamic scenes. Furthermore, the local receptive fields of CNNs inadequately capture global context. To address these issues, we propose MPCFormer, a motion-aware Transformer method via multi-channel preference filtering and multi-scale consensus smoothing for robust model fitting. It reformulates robust model fitting as a joint optimization of point classification and model estimation by integrating correspondence learning and embedding spatiotemporal motion cues, eliminating iterative hypothesis sampling. Specifically, we design a motion preference filter to explore multi-channel motion information by residual-connected Transformer layers. It explicitly encodes data preference distributions for models via multi-head preference attention, generating confidence scores to adaptively suppress outlier interference and enhance model robustness. Additionally, we present a pyramid consensus smoother with multi-scale Transformer encoding. It hierarchically captures local-to-global motion consistency through a sparse feature pyramid, effectively resolving motion ambiguity from spatial discontinuities. This module enables precise inlier identification and reliable model estimation through multi-head consensus attention. Extensive experiments demonstrate that MPCFormer outperforms state-of-the-art baselines by 4.68% mAP@5$^{\circ }$∘, 1.89% AUC@3 pixel, and 1.52% F-score, even at extreme outlier ratios (up to 95%).