CoReMatch: Recurrent Continuous Coordinate Refinement for Real-Time Robotic Feature Matching
Overview
This paper presents CoReMatch, a recurrent framework for real-time robotic feature matching that refines correspondences directly in continuous coordinate space. By using a Deep Aggregated Transformer at the coarse stage and a lightweight regression head to sharpen localization, the method outperforms Efficient LoFTR in accuracy while running more than 10x faster on embedded edge hardware, demonstrating an efficient and scalable approach for robotic perception tasks like visual localization, mapping, SLAM, and autonomous navigation.
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
No current evidence-backed claims. Missing information remains not reported.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalCoReMatch: Recurrent Continuous Coordinate Refinement for Real-Time Robotic Feature Matching
This paper presents CoReMatch, a recurrent framework for real-time robotic feature matching that refines correspondences directly in continuous coordinate space. By using a Deep Aggregated Transformer at the coarse stage and a lightweight regression head to sharpen localization, the method outperforms Efficient LoFTR in accuracy while running more than 10x faster on embedded edge hardware, demonstrating an efficient and scalable approach for robotic perception tasks like visual localization, mapping, SLAM, and autonomous navigation.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).