Adaptive Graph Attention-Guided Parallel Sampling and Embedded Selection for Multi-Model Fitting

Multi-model fitting is a fundamental challenge in computer vision, where real-world data often contains severe gross outliers and pseudo-outliers. Existing methods rely on inefficient sequential hypothesize-and-verify frameworks that require a predefined number of models and inlier thresholds-parameters that are difficult to determine in practical scenes. To overcome these limitations, we propose a novel Adaptive Graph Attention-guided parallel multi-model fitting method (AGASAC) that jointly learns local and global features, performs parallel hypothesis sampling, and executes confidence-embedded model selection. Specifically, we design a dual-confidence graph attention module that models data relationships using an adaptive graph attention network. This module computes minimal-set confidence and quality confidence to guide the multi-model fitting process, eliminating manual parameter tuning. Additionally, we propose a parallel discriminative sampling module that leverages minimal-set confidence to concurrently sample hypotheses. By enforcing a quantized consensus constraint, this module maximizes inter-model variance while minimizing intra-model discrepancy. It enables computationally efficient hypothesis generation and pseudo-outlier suppression. To obtain high-quality models, we present a quality-embedded selection module that integrates quality confidence into the joint optimization of model selection and data clustering. Extensive experiments show that the proposed method achieves a lower transfer error of 0.39 pixels and a 36.92% runtime reduction, surpassing state-of-the-art methods. The code is available at https://github.com/YWY-Vivian/AGASAC.