Graph Context Transformation Learning for Progressive Correspondence
  Pruning
Junwen Guo Guobao Xiao Shiping Wang Jun Yu
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/.