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.gitignore

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+.idea

README.md

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 license: apache-2.0
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+
+## Abstract
+
+Image matching for both cross-view and cross-modality plays a critical role in multimodal perception. In practice, the
+modality gap caused by different imaging systems/styles poses great challenges to the matching task. Existing works try
+to extract invariant features for specific modalities and train on limited datasets, showing poor generalization. In
+this paper, we present MINIMA, a unified image matching framework for multiple cross-modal cases. Without pursuing fancy
+modules, our MINIMA aims to enhance universal performance from the perspective of data scaling up. For such purpose, we
+propose a simple yet effective data engine that can freely produce a large dataset containing multiple modalities, rich
+scenarios, and accurate matching labels. Specifically, we scale up the modalities from cheap but rich RGB-only matching
+data, by means of generative models. Under this setting, the matching labels and rich diversity of the RGB dataset are
+well inherited by the generated multimodal data. Benefiting from this, we construct MD-syn, a new comprehensive dataset
+that fills the data gap for general multimodal image matching. With MD-syn, we can directly train any advanced matching
+pipeline on randomly selected modality pairs to obtain cross-modal ability. Extensive experiments on in-domain and
+zero-shot matching tasks, including 19 cross-modal cases, demonstrate that our MINIMA can significantly outperform the
+baselines and even surpass modality-specific methods. The dataset and code are available
+at https://github.com/LSXI7/MINIMA .

minima_lightglue.pth

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minima_loftr.ckpt

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minima_roma.pth

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