CrossFeat: Bridging Imaging Modalities in Feature Descriptor Space
Most advances in keypoint descriptions address monomodal settings, where image variations arise from viewpoint, illumination, or contrast changes. Multimodal scenarios involve images produced by fundamentally different sensing processes, such as multispectral imaging, RGB-depth, satellite imagery, or medical imaging, causing the same structures to appear differently. A common solution to cross-modal description is to train descriptors for each modality pair, which requires retraining whenever the modalities change, or to train large models, which incur a significant increase in runtime. Instead, we propose CrossFeat, a framework that enables an existing monomodal descriptor to operate acr...
| Authors | Paul Schneider, Nazim Haouchine |
|---|---|
| Primary category | cs.CV |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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