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author:

Guo, Junwen (Guo, Junwen.) [1] | Xiao, Guobao (Xiao, Guobao.) [2] | Wang, Shiping (Wang, Shiping.) [3] (Scholars:王石平) | Yu, Jun (Yu, Jun.) [4]

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EI

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. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Keyword:

Artificial intelligence

Community:

  • [ 1 ] [Guo, Junwen]School of Electronics and Information Engineering, Tongji University, China
  • [ 2 ] [Guo, Junwen]College of Computer and Data Science, Fuzhou University, China
  • [ 3 ] [Xiao, Guobao]School of Electronics and Information Engineering, Tongji University, China
  • [ 4 ] [Wang, Shiping]College of Computer and Data Science, Fuzhou University, China
  • [ 5 ] [Yu, Jun]School of Computer Science and Technology, Hangzhou Dianzi University, China

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ISSN: 2159-5399

Year: 2024

Issue: 3

Volume: 38

Page: 1968-1975

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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