MatcherCompass: A Deployment-Aware Benchmark for Choosing Image Matchers on Real Robots
zhenjun_zhao · x · 2026-09-23
A new arXiv paper (2609.25688) presents MatcherCompass, a deployment-aware benchmark for selecting local feature matchers in field robotics.
- Under common input and pose-evaluation procedures, it compares nine classical and learned matching pipelines across four image resolutions and supported numerical precisions; visual conditions cover viewpoint variation, day–night visible and thermal matching, and daytime visible–thermal matching.
- Pose accuracy is measured via error–recall AUC at 5°/10°/20°, plus runtime, GPU memory and energy per image pair on four GPU platforms spanning workstations and onboard computers.
- Key finding: changes in hardware, resolution, and precision can push a matcher across a runtime budget boundary, altering feasible choices. Results are organized into a selection guide returning all configurations meeting user-specified time/resource limits with their accuracy.
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