AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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SGFormer (Structure-Guided Transformer), a novel structure-aware matching network that adaptively updates attention on features near salient structure in overlapping regions that significantly mitigates attention divergence and improves matching accuracy is proposed.
This work proposes a novel Cross-Coordinate Correspondences Pruning (CCP) strategy to acquire sufficient inliers while ensuring a low outlier ratio, and designs a Multi-Density Point Ensemble (MDPE) strategy that consolidates and deduplicates pruned coarse correspondences across varying point cloud densities.
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