Correspondence pruning aims to remove false correspondences (outliers) from an initial putative correspondence set. This process holds significant importance and serves as a fundamental step in various applications within the fields of remote sensing and photogrammetry. The presence of noise, illumination changes, and small overlaps in remote sensing images frequently result in a substantial number of outliers within the initial set, thereby rendering the correspondence pruning notably challenging. Although the spatial consensus of correspondences has been widely used to determine the correctness of each correspondence, achieving uniform consensus can be challenging due to the uneven distribution of correspondences. Existing works have mainly focused on either local or global consensus, with a very small perspective or large perspective, respectively. They often ignore the moderate perspective between local and global consensus, called group consensus, which serves as a buffering organization from local to global consensus, hence leading to insufficient correspondence consensus aggregation. To address this issue, we propose a multi-granularity consensus network (MGCNet) to achieve consensus across regions of different scales, which leverages local, group, and global consensus to accomplish robust and accurate correspondence pruning. Specifically, we introduce a GroupGCN module that randomly divides the initial correspondences into several groups and then focuses on group consensus and acts as a buffer organization from local to global consensus. Additionally, we propose a Multi-level Local Feature Aggregation Module that adapts to the size of the local neighborhood to capture local consensus and a Multi-order Global Feature Module to enhance the richness of the global consensus. Experimental results demonstrate that MGCNet outperforms state-of-the-art methods on various tasks, highlighting the superiority and great generalization of our method. In particular, we achieve 3.95% and 8.5% mAP5° improvement without RANSAC on the YFCC100M dataset in known and unknown scenes for pose estimation, compared to the second-best models (MSA-LFC and CLNet). Source code: https://github.com/1211193023/MGCNet.

中文翻译：

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MGCNet：用于遥感图像对应剪枝的多粒度共识网络


对应修剪旨在从初始假定的对应集中删除错误的对应（异常值）。这个过程具有重要意义，是遥感和摄影测量领域各种应用的基本步骤。遥感图像中存在的噪声、照明变化和小重叠经常会导致初始集中出现大量异常值，从而使对应修剪变得非常具有挑战性。尽管对应关系的空间一致性已被广泛用于确定每个对应关系的正确性，但由于对应关系的分布不均匀，实现统一一致可能具有挑战性。现有工作主要关注本地或全球共识，分别具有非常小的视角或大的视角。他们经常忽视本地共识和全球共识之间的中度视角，称为群体共识，它充当从本地共识到全球共识的缓冲组织，从而导致对应共识聚合不足。为了解决这个问题，我们提出了一个多粒度共识网络 （MGCNet） 来实现不同规模区域之间的共识，它利用本地、组和全球共识来完成稳健和准确的对应修剪。具体来说，我们引入了一个 GroupGCN 模块，该模块将初始对应随机分为几组，然后专注于群体共识，并充当从本地共识到全球共识的缓冲组织。 此外，我们提出了一个多级局部特征聚合模块，它适应局部邻域的大小来捕获局部共识，以及一个多阶全局特征模块来增强全局共识的丰富性。实验结果表明，MGCNet 在各种任务上都优于最先进的方法，凸显了我们方法的优越性和广泛的通用性。特别是，与次优模型（MSA-LFC 和 CLNet）相比，我们在已知和未知场景的 YFCC100M 数据集上在没有 RANSAC 的情况下实现了 3.95% 和 8.5% 的 mAP5° 改进。源代码：https://github.com/1211193023/MGCNet。