MINIMA: Modality Invariant Image Matching (2412.19412v2)

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.

• The paper introduces a data-driven framework that leverages generative models to synthesize diverse synthetic datasets for cross-modal image matching.
• Experimental results across 19 cross-modal cases demonstrate significant improvements in both in-domain and zero-shot matching tasks.
• The research reduces reliance on manually annotated datasets, paving the way for more efficient and scalable multimodal perception systems.

Modality Invariant Image Matching: Advancements and Insights

The paper "MINIMA: Modality Invariant Image Matching" presents a methodologically rigorous advancement in the field of multimodal image matching. The work focuses on addressing the inherent challenges associated with cross-view and cross-modality image matching, a problem of significance within multimodal perception systems. The paper critiques current methodologies for their reliance on invariant features tailor-made for specific modalities and the resultant poor generalization due to limited dataset training.

Overview of the MINIMA Framework

The authors introduce MINIMA, a unified image matching framework designed to enhance performance across multiple cross-modal scenarios. The framework shifts focus from complex model architectures to data-centric processes. A novel data engine is proposed, capable of dynamically generating extensive datasets from RGB sources via generative models. This approach allows for the creation of diverse datasets spanning multiple modalities with rich scene variations and consistent matching labels, effectively bridging the gap between practical cross-modal image data requirements and the availability of annotated datasets.

A significant contribution of this research is the development of MD-syn, an expansive synthetic dataset that complements traditional multimodal image matching datasets. The dataset is generated by scaling up from MegaDepth's RGB-only data to cover modalities like Infrared, Depth, Event, Normal, and artistic styles, embodying both diversity and precise labeling. This scalability is achieved without resorting to traditional painstaking and manual data collection techniques but leverages recent advances in generative models for efficient image synthesis.

Experimental Validation and Results

The authors validate MINIMA across diverse multimodal scenarios encompassing 19 cross-modal cases. The experimental results on MD-syn and several real-world multimodal datasets showcase the superior performance of MINIMA over existing, state-specific methods. Notably, MINIMA demonstrates significant improvements in both in-domain and zero-shot matching tasks, which test robustness against previously unseen modality combinations. The framework outperforms baseline methods consistently, achieving higher accuracy and efficiency as indicated by metrics like AUC for pose errors and projective errors.

Theoretical and Practical Implications

Practically, the paper highlights the potential for MINIMA to reduce the dependency on large-scale annotated datasets, providing a sustainable path forward for real-world applications in image fusion, visual localization, and target recognition. Theoretically, the findings emphasize the critical role of data quality and diversity over complex modeling, posing future questions on the broader applicability of generative models in image data augmentation for diverse computational tasks.

Future Directions

This research opens multiple avenues for future exploration. Primarily, the incorporation of real-time generative model adaptations could further enhance the performance of real-world applications, given the modality-specific dynamics in rapid-changing scenes. Moreover, expanding the framework to seamlessly adapt to more specialized domains like remote sensing and medical imaging could significantly boost precision in these critical areas.

Conclusion

In summary, "MINIMA: Modality Invariant Image Matching" represents a substantial contribution to multimodal image matching, presenting a pivot from complexity in model architecture towards data-driven solutions. The adoption of a versatile and scalable data generation engine, combined with rigorous empirical validation, invites a rethinking of how modality-agnostic performance in image matching can be achieved. The implications of this research are far-reaching, promising enhanced accuracy and robustness in multimodal perception tasks across various applied domains.