HomoFM: Deep Homography Estimation with Flow Matching

• URL: http://arxiv.org/abs/2601.18222v1
• Date: Mon, 26 Jan 2026 07:17:32 GMT
• Title: HomoFM: Deep Homography Estimation with Flow Matching
• Authors: Mengfan He, Liangzheng Sun, Chunyu Li, Ziyang Meng,
• Abstract summary: HomoFM is a new framework that introduces the flow matching technique from generative modeling into the homography estimation task.<n>We show that HomoFM outperforms state-of-the-art methods in both estimation accuracy and robustness on standard benchmarks.
• Score: 2.0260360833154913
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep homography estimation has broad applications in computer vision and robotics. Remarkable progresses have been achieved while the existing methods typically treat it as a direct regression or iterative refinement problem and often struggling to capture complex geometric transformations or generalize across different domains. In this work, we propose HomoFM, a new framework that introduces the flow matching technique from generative modeling into the homography estimation task for the first time. Unlike the existing methods, we formulate homography estimation problem as a velocity field learning problem. By modeling a continuous and point-wise velocity field that transforms noisy distributions into registered coordinates, the proposed network recovers high-precision transformations through a conditional flow trajectory. Furthermore, to address the challenge of domain shifts issue, e.g., the cases of multimodal matching or varying illumination scenarios, we integrate a gradient reversal layer (GRL) into the feature extraction backbone. This domain adaptation strategy explicitly constrains the encoder to learn domain-invariant representations, significantly enhancing the network's robustness. Extensive experiments demonstrate the effectiveness of the proposed method, showing that HomoFM outperforms state-of-the-art methods in both estimation accuracy and robustness on standard benchmarks. Code and data resource are available at https://github.com/hmf21/HomoFM.

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