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Modal Invariance in Image Matching: The MINIMA Framework

Image matching, the pixel-accurate mapping of image points between two views of the same object or scene, is a fundamental technology in computer vision. It plays a crucial role in areas such as 3D reconstruction, robotics, and augmented reality. Image matching becomes particularly challenging when the images originate from different modalities, for example, RGB and infrared images. Such multimodal image matching is essential for applications like image fusion, medical imaging, and autonomous navigation. The different physical properties of the imaging processes lead to so-called modality gaps, which complicate the extraction of common features.

Previous approaches to multimodal image matching often focused on the development of modality-specific feature extractors and training on limited datasets. This specialization leads to poor generalizability of the models, limiting their use in new, unknown modality combinations.

A promising new approach is presented by the MINIMA framework (Modality Invariant Image Matching). MINIMA aims to develop a universal image matching solution for diverse modality combinations. Instead of using complex, modality-specific modules, MINIMA relies on scaling the training data. The core of the framework is a "Data Engine" that synthetically generates multimodal datasets from existing RGB image pairs. By using generative models, the existing RGB data is augmented with additional modalities such as infrared, depth information, or event camera data. The original correspondence information is retained, enabling efficient training of matching pipelines.

Using this Data Engine, the MD-syn dataset was created, which covers a wide range of scenarios and modalities, thus closing the data gap in multimodal image matching. By training on MD-syn, existing matching algorithms can be directly adapted for multimodal use without complex adjustments to the architecture.

The MINIMA Data Engine: Key to Modal Invariance

The MINIMA Data Engine uses generative models to create synthetic multimodal data from RGB image pairs. This approach enables a cost-effective expansion of existing datasets with additional modalities. The generated data inherits the correspondence information of the original RGB images, enabling direct training of matching pipelines on the multimodal data.

The Data Engine not only generates additional modalities but also variations within the individual modalities. This increases the robustness of the trained models against different recording conditions and image styles.

Evaluation and Results

The MINIMA framework was evaluated using 19 different modality combinations for both known and unknown (zero-shot) scenarios. The results show a significant improvement over existing modality-specific methods. MINIMA demonstrates high generalizability and achieves convincing results even in zero-shot scenarios.

The improvements achieved by MINIMA underscore the potential of data-driven approaches in multimodal image matching. The Data Engine enables efficient and cost-effective generation of training data, which allows for the development of robust and generalizable matching models.

Outlook

The MINIMA framework represents an important step towards a universal solution for multimodal image matching. Scaling the training data through the Data Engine opens up new possibilities for the development of robust and generalizable matching algorithms. Future research could focus on expanding the Data Engine with further modalities and optimizing the generative models.

Bibliography Jiang, X., Ren, J., Li, Z., Zhou, X., Liang, D., & Bai, X. (2024). MINIMA: Modality Invariant Image Matching. arXiv preprint arXiv:2412.19412. https://arxiv.org/abs/2412.19412 https://arxiv.org/html/2412.19412v1 https://github.com/LSXI7/MINIMA https://www.alphaxiv.org/abs/2412.19412 https://paperswithcode.com/author/xin-zhou https://github.com/ericzzj1989/Awesome-Image-Matching https://www.catalyzex.com/s/Image%20Matching https://openreview.net/forum?id=ceUtIUfotv https://www.mdpi.com/2072-4292/16/16/2880 Wachinger, C., & Navab, N. (2009). Variational Methods for Multimodal Image Matching. In Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on (pp. 1512-1519). IEEE. ```