EarthBridge: A Solution for 4th Multi-modal Aerial View Image Challenge Translation Track
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Abstract (arXiv)
Cross-modal image-to-image translation among Electro-Optical (EO), Infrared (IR), and Synthetic Aperture Radar (SAR) sensors is essential for comprehensive multi-modal aerial-view analysis. However, translating between these modalities is notoriously difficult due to their distinct electromagnetic signatures and geometric characteristics. This paper presents EarthBridge, a high-fidelity translation framework developed for the 4th Multi-modal Aerial View Image Challenge -- Translation (MAVIC-T). We explore two distinct methodologies: Diffusion Bridge Implicit Models (DBIM), which we generalize using non-Markovian bridge processes for high-quality deterministic sampling, and Contrastive Unpaired Translation (CUT), which utilizes contrastive learning for structural consistency. Our EarthBridge framework employs a channel-concatenated UNet denoiser trained with Karras-weighted bridge scalings and a specialized "booting noise" initialization to handle the inherent ambiguity in cross-modal mappings. We evaluate these methods across all four challenge tasks (SARrightarrowEO, SARrightarrowRGB, SARrightarrowIR, RGBrightarrowIR), achieving superior spatial detail and spectral accuracy. Our solution achieved a composite score of 0.38, securing the second position on the MAVIC-T leaderboard. Code is available at https://github.com/Bili-Sakura/EarthBridge-Preview.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 55 references that are in the map. First posted on arXiv 6 Mar 2026.
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References · 26
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- 2022Dual Diffusion Implicit Bridges for Image-to-Image Translationcited by 212
- 2023Denoising Diffusion Bridge Modelscited by 171
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