View PDF (https://arxiv.org/pdf/2606.31077)HTML (experimental) (https://arxiv.org/html/2606.31077v1) Abstract:Multi-modal image matching is essential for visual localization and multi-sensor fusion, but it is hindered by the scarcity of large-scale training data with precise geometric annotations. Existing real-world datasets suffer from prohibitive costs, limited scene diversity, and errors in SfM-MVS pipelines, while synthetic methods struggle to maintain 3D geometric consistency or achieve photorealistic appearance. To address this, we propose AnyMatch, a novel framework that leverages abundant, easily accessible single-view images at minimal cost to generate rich multi-modal training data. AnyMatch integrates monocular depth estimation, 3D reprojection, diffusion-based inpainting, and crossmodal image translation to synthesize multi-view, multi-modal image pairs with 3D geometric fidelity. Crucially, our method provides annotations that strictly adhere to 3D geometric consistency through explicit 3D reprojection, avoiding SfM-MVS error accumulation. Furthermore, AnyMatch offers strong scalability, enabling controllable scene diversity and annotation difficulty via adjustable input and camera parameters. We construct Any-syn, a large-scale synthetic multi-modal dataset using AnyMatch. Experimental results show that matching networks (e.g., LoFTR, EDM, RoMa) fine-tuned on Any-syn achieve substantial performance gains on multi-modal benchmarks, exhibiting superior generalization and robustness compared to models trained on existing data. Subjects:Computer Vision and Pattern Recognition (cs.CV) Cite as:arXiv:2606.31077 (https://arxiv.org/abs/2606.31077) [cs.CV] (or arXiv:2606.31077v1 (https://arxiv.org/abs/2606.31077v1) [cs.CV] for this version) https://doi.org/10.48550/arXiv.2606.31077 arXiv-issued DOI via DataCite (pending registration) Submission history From: Zizhuo Li [view email (https://arxiv.org/show-email/7fc3339a/2606.31077)] • *[v1]** Tue, 30 Jun 2026 03:06:58 UTC (41,297 KB)