GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training
Descriptor-free visual localization eliminates high-dimensional descriptor storage, preserves scene privacy, and simplifies map maintenance, yet its accuracy still lags far behind descriptor-based pipelines. We identify this gap to insufficient geometric discriminability in geometry-only matching. Without visual appearance, current methods underutilize local geometry cues, lack the global context among keypoints, and overfit to a single keypoint detector. We further observe that descriptor-free matching naturally enables multi-detector training, as heterogeneous keypoints can be optimized in a
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- PossiblePossibly related (embedding) · 46%Showcase: geolocating a dashcam video without GPS, only from the footage [P] →
- LinkedLinked via arxiv author · 85%Yejun Zhang →
“GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training”
- LinkedLinked via arxiv author · 85%Xinjue Wang →
“GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training”
- LinkedLinked via arxiv author · 85%Zihan Wang →
“GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training”
- LinkedLinked via arxiv author · 85%Esa Rahtu →
“GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training”
- LinkedLinked via arxiv author · 85%Juho Kannala →
“GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”