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 shared geometry-only space without aligning descriptor spaces. Building on these insights, we pr...
| Authors | Yejun Zhang, Xinjue Wang, Zihan Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 July 2026 |
| Last updated at source | 22 August 2026 |
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