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: this https URL

提出了图上下文转换网络（GCT-Net），通过增强上下文信息进行共识引导以实现渐进式对应关系修剪，并使用图上下文增强变换器和图上下文引导变换器来放大每个图上下文的特征以强调独特的以及共享的基本信息，并通过全局域中的重新校准的图上下文进行引导。实验结果表明，在室内和室外数据集上，GCT-Net 相对于当前最先进的方法在离群值去除和相对姿态估计方面表现出更好的性能。