LeanGate is a lightweight feed-forward network that predicts geometric utility scores to skip over 90% of redundant frames in GFM-based monocular SLAM, reducing tracking FLOPs by 85% and achieving 5x speedup while maintaining accuracy.
D2-net: A trainable cnn for joint description and detection of local features,
2 Pith papers cite this work. Polarity classification is still indexing.
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A semi-dense image matching pipeline adds scale adaptability via score-matrix hints at the coarse stage and local flow consistency via gradient loss at the fine stage.
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Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring
LeanGate is a lightweight feed-forward network that predicts geometric utility scores to skip over 90% of redundant frames in GFM-based monocular SLAM, reducing tracking FLOPs by 85% and achieving 5x speedup while maintaining accuracy.
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Improving Local Feature Matching by Entropy-inspired Scale Adaptability and Flow-endowed Local Consistency
A semi-dense image matching pipeline adds scale adaptability via score-matrix hints at the coarse stage and local flow consistency via gradient loss at the fine stage.