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MGNet: Learning Correspondences via Multiple Graphs (2401.04984v1)

Published 10 Jan 2024 in cs.CV

Abstract: Learning correspondences aims to find correct correspondences (inliers) from the initial correspondence set with an uneven correspondence distribution and a low inlier rate, which can be regarded as graph data. Recent advances usually use graph neural networks (GNNs) to build a single type of graph or simply stack local graphs into the global one to complete the task. But they ignore the complementary relationship between different types of graphs, which can effectively capture potential relationships among sparse correspondences. To address this problem, we propose MGNet to effectively combine multiple complementary graphs. To obtain information integrating implicit and explicit local graphs, we construct local graphs from implicit and explicit aspects and combine them effectively, which is used to build a global graph. Moreover, we propose Graph~Soft~Degree~Attention (GSDA) to make full use of all sparse correspondence information at once in the global graph, which can capture and amplify discriminative features. Extensive experiments demonstrate that MGNet outperforms state-of-the-art methods in different visual tasks. The code is provided in https://github.com/DAILUANYUAN/MGNet-2024AAAI.

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Citations (3)
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Summary

  • The paper introduces a multi-graph network that uses the Graph Soft Degree Attention mechanism to effectively distinguish inliers from outliers.
  • The method integrates local and global graph representations, significantly improving camera pose estimation, homography, and visual localization tasks.
  • Empirical results demonstrate that MGNet achieves superior performance with fewer parameters, underlining its efficiency and practical impact.

Introduction to MGNet

Understanding pixel-wise correspondences between different images is a cornerstone in the field of computer vision. These correspondences are essential for a variety of tasks such as image stitching, SLAM, and 3D reconstruction. The challenge, however, lies in identifying correct correspondences, often called inliers, amidst a plethora of false ones or outliers, particularly when the inlier rate is low and distribution of potential matches is uneven - a common occurrence in real-world scenarios.

Understanding Multiple Graphs

Traditional approaches of graph-based neural networks (GNNs) often represent correspondences as a graph and apply a singular approach to cull inliers from outliers. Recent advancements have integrated local graph interpretations into a larger global framework. Yet, this has typically overlooked the complementary benefits of constructing multiple graph types simultaneously. Such a multifaceted approach can capture the nuanced relationships between sparse correspondences more effectively.

MGNet leverages this insight and presents a novel network architecture that harnesses the power of multiple, complementary graphs. It introduces an innovative Graph Soft Degree Attention (GSDA) mechanism to utilize sparse correspondence information in a global graph setting. This enables the network to spotlight and intensify discriminative features, instrumental in distinguishing inliers.

Breaking Down the Approach

To capture both implicit and explicit information, MGNet constructs local graphs through these dual perspectives and subsequently explores the interrelations between them. Through GSDA, MGNet excels at synthesizing global information to spotlight and amplify more discriminative cues. As depicted within the research, the method distinctly outperforms its predecessors across multiple visual tasks. With an emphasis on adaptability and finesse, MGNet realizes a greater utility of GNNs in pinning down sparse correspondences.

Empirical Validation

Extensive experimentation underlines MGNet's superiority over state-of-the-art methods in camera pose estimation, homography estimation, and visual localization. Strikingly, its performance peaks even when utilizing fewer parameters, an indication of its efficiency and effectiveness. The provided open-source codebase encourages further exploration and development within the community.

Conclusion

The development of MGNet marks a significant stride in correspondence learning. By embracing a multi-graph perspective and introducing the Graph Soft Degree Attention mechanism, it sets a new benchmark in the field. Its exceptional results across various tests bear testament to its robust performance, holding promise for a plethora of applications within both academia and industry.

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GitHub

  1. DAILUANYUAN/MGNet-2024AAAI · GitHub