Most of existing correspondence pruning methods only concentrate on gathering the context information as much as possible while neglecting effective ways to utilize such information. In order to tackle this dilemma, in this paper we propose Graph Context Transformation Network (GCT-Net) enhancing context information to conduct consensus guidance for progressive correspondence pruning. Specifically, we design the Graph Context Enhance Transformer which first generates the graph network and then transforms it into multi-branch graph contexts. Moreover, it employs self-attention and cross-attention to magnify characteristics of each graph context for emphasizing the unique as well as shared essential information. To further apply the recalibrated graph contexts to the global domain, we propose the Graph Context Guidance Transformer. This module adopts a confident-based sampling strategy to temporarily screen high-confidence vertices for guiding accurate classification by searching global consensus between screened vertices and remaining ones. The extensive experimental results on outlier removal and relative pose estimation clearly demonstrate the superior performance of GCT-Net compared to state-of-the-art methods across outdoor and indoor datasets. The source code will be available at: https://github.com/guobaoxiao/GCT-Net/.

翻译：现有的大多数对应点筛选方法仅专注于尽可能多地收集上下文信息，却忽略了有效利用这些信息的方式。为解决这一困境，本文提出图上下文变换网络（GCT-Net），通过增强上下文信息来引导渐进式对应点筛选中的一致性共识。具体而言，我们设计了图上下文增强Transformer：首先构建图网络，然后将其转换为多分支图上下文；进一步采用自注意力与交叉注意力机制放大每个图上下文的特征，以强调独特与共享的关键信息。为将重新校准后的图上下文应用于全局域，我们提出图上下文引导Transformer模块。该模块采用基于置信度的采样策略，临时筛选出高置信度顶点，通过搜索筛选顶点与剩余顶点间的全局一致性来实现精确分类。在离群点剔除与相对位姿估计任务上的大量实验结果表明，GCT-Net在室内外数据集上均显著优于现有最先进方法。源代码将发布于：https://github.com/guobaoxiao/GCT-Net/。