Correspondence pruning aims to identify inliers from correspondences severely disturbed by outliers. Although Transformers and graph neural networks have shown impressive results in this field, they are either limited by a narrow receptive field or encounter quadratic computational complexity. To tackle this challenge, this work pioneers the integration of state space model into correspondence pruning task, proposing a Mamba-based framework named MambaMatch. Specifically, to address the limitations of the Mamba architecture in local consensus modeling, we proposes a multi-scale scanning strategy. It first employs an adaptive clustering algorithm to map origin correspondences into spatially coherent feature clusters, constructing a dual-representation space encompassing both full-scale and clustered-scale features. Bidirectional scan operations are then performed at both scales: 1) full-scale scan preserves global structural context, and 2) clustered-scale scan enhances local consistency. Subsequently, a Multi-Scale Interaction layer is designed to dynamically fuse dual-scale features via a cross-attention mechanism, further integrated with a Gated Feed-Forward Network to significantly improve the network's feature discrimination capability. Extensive experiments validate that MambaMatch surpasses state-of-the-art approaches across multiple benchmarks for two-view geometry estimation. Furthermore, MambaMatch exhibits robust generalization across diverse scenarios, tasks, and feature extractors. The source code is available at: https://github.com/mxyttkx/MambaMatch.

We present a novel deep hypergraph modeling architecture (called DHM-Net) for feature matching in this paper. Our network focuses on learning reliable correspondences between two sets of initial feature points by establishing a dynamic hypergraph structure that models group-wise relationships and assigns weights to each node. Compared to existing feature matching methods that only consider pair-wise relationships via a simple graph, our dynamic hypergraph is capable of modeling nonlinear higher-order group-wise relationships among correspondences in an interaction capturing and attention representation learning fashion. Specifically, we propose a novel Deep Hypergraph Modeling block, which initializes an overall hypergraph by utilizing neighbor information, and then adopts node-to-hyperedge and hyperedge-to-node strategies to propagate interaction information among correspondences while assigning weights based on hypergraph attention. In addition, we propose a Differentiation Correspondence-Aware Attention mechanism to optimize the hypergraph for promoting representation learning. The proposed mechanism is able to effectively locate the exact position of the object of importance via the correspondence aware encoding and simple feature gating mechanism to distinguish candidates of inliers. In short, we learn such a dynamic hypergraph format that embeds deep group-wise interactions to explicitly infer categories of correspondences. To demonstrate the effectiveness of DHM-Net, we perform extensive experiments on both real-world outdoor and indoor datasets. Particularly, experimental results show that DHM-Net surpasses the state-of-the-art method by a sizable margin. Our approach obtains an 11.65% improvement under error threshold of 5° for relative pose estimation task on YFCC100M dataset. Code will be released at https://github.com/CSX777/DHM-Net.

Establishing reliable correspondences via a deep learning network is an important task in remote sensing, photogrammetry and other computer vision fields. It usually requires mining the relationship among correspondences to aggregate both local and global context. However, current methods are insufficient to effectively acquire context information with high reliability. In this paper, we propose a graph context attention based network (called GCA-Net) to capture and leverage abundant contextual information for feature matching. Specifically, we design a graph context attention block which generates multi-path graph contexts and softly fuses them to combine respective advantages. In addition, for building the graph context containing stronger representation ability and outlier resistance ability, we further design a local-global channel mining block to gather context information by focusing on the significant part as well as to mine dependencies among channels of correspondences in both local and global aspects. The proposed GCA-Net is able to effectively infer the probability of correspondences being inliers or outliers and estimate the essential matrix meanwhile. Extensive experimental results for outlier removal and relative pose estimation demonstrate that GCA-Net outperforms the state-of-art methods on both outdoor and indoor datasets (i.e.,YFCC100M and SUN3D). In addition, experiments extended to remote sensing and point cloud scenes also demonstrate the powerful generalization capability of our network.