AnyMatch synthesizes large-scale geometrically consistent multi-modal image pairs from single-view images, enabling fine-tuned matching networks to achieve substantial gains on benchmarks.
arXiv preprint arXiv:2501.07556 (2025)
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2026 4representative citing papers
RBE-Flow recasts dense cross-modal flow estimation as closed-loop recurrent Bayesian estimation on learned feature manifolds with uncertainty-adaptive updates and achieves SOTA on three registration benchmarks.
A single event-camera feature matching model trained on synthetic data achieves zero-shot wide-baseline correspondence across unseen datasets with 37.7% improvement over prior methods.
citing papers explorer
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AnyMatch: Supercharging Universal Multi-Modal Image Matching with Large-Scale Single-View Images
AnyMatch synthesizes large-scale geometrically consistent multi-modal image pairs from single-view images, enabling fine-tuned matching networks to achieve substantial gains on benchmarks.
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RBE-Flow: Recurrent Bayesian Estimation on Feature Manifolds for Cross-Modal Registration
RBE-Flow recasts dense cross-modal flow estimation as closed-loop recurrent Bayesian estimation on learned feature manifolds with uncertainty-adaptive updates and achieves SOTA on three registration benchmarks.
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Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras
A single event-camera feature matching model trained on synthetic data achieves zero-shot wide-baseline correspondence across unseen datasets with 37.7% improvement over prior methods.
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