Graph Context Transformation Learning for Progressive Correspondence Pruning

• URL: http://arxiv.org/abs/2312.15971v1
• Date: Tue, 26 Dec 2023 09:43:30 GMT
• Title: Graph Context Transformation Learning for Progressive Correspondence Pruning
• Authors: Junwen Guo, Guobao Xiao, Shiping Wang, Jun Yu
• Abstract summary: 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. To further apply the recalibrated graph contexts to the global domain, we propose the Graph Context Guidance Transformer.
• Score: 26.400567961735234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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