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Paper#Computer Vision, Deep Learning, Correspondence Learning🔬 ResearchAnalyzed: Jan 3, 2026 18:46

SC-Net: Improved Correspondence Learning with Context

Published:Dec 29, 2025 13:56
1 min read
ArXiv

Analysis

This paper introduces SC-Net, a novel network for two-view correspondence learning. It addresses limitations of existing CNN-based methods by incorporating spatial and cross-channel context. The proposed modules (AFR, BFA, PAR) aim to improve position-awareness, robustness, and motion field refinement, leading to better performance in relative pose estimation and outlier removal. The availability of source code is a positive aspect.
Reference

“SC-Net outperforms state-of-the-art methods in relative pose estimation and outlier removal tasks on YFCC100M and SUN3D datasets.”

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Paper#Computer Vision🔬 ResearchAnalyzed: Jan 3, 2026 18:55

MGCA-Net: Improving Two-View Correspondence Learning

Published:Dec 29, 2025 10:58
1 min read
ArXiv

Analysis

This paper addresses limitations in existing methods for two-view correspondence learning, a crucial task in computer vision. The proposed MGCA-Net introduces novel modules (CGA and CSMGC) to improve geometric modeling and cross-stage information optimization. The focus on capturing geometric constraints and enhancing robustness is significant for applications like camera pose estimation and 3D reconstruction. The experimental validation on benchmark datasets and the availability of source code further strengthen the paper's impact.
Reference

“MGCA-Net significantly outperforms existing SOTA methods in the outlier rejection and camera pose estimation tasks.”

PermalinkArXiv