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arXiv Computer Vision By Adam D. Hines, Gokul B. Nair, Nicol\'as Marticorena, Michael Milford, Tobias Fischer

EventGeM: Global-to-Local Feature Matching for Event-Based Visual Place Recognition

Read the original on arXiv Computer Vision →

EventGeM introduces a global‑to‑local feature fusion pipeline for event‑based visual place recognition, combining whole‑image feature detection with 2D homography‑based re‑ranking via RANSAC. It adds a regional generalized mean (GeM) pooling layer that learns to extract the most relevant spatial features from event streams, producing a compact global descriptor trained on the NYC‑Event‑VPR dataset. The method demonstrates significant improvements in viewpoint‑robust localization, achieving 7–43 percentage point gains in Recall@1 over the strongest baseline and real‑time performance on a robotic platform.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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