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arXiv · 2601.02730

HOLO: Homography-Guided Pose Estimator Network for Fine-Grained Visual Localization on SD Maps

Xuchang Zhong·Xu Cao·Jinke Feng·Hao Fang
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Abstract

Visual localization on standard-definition (SD) maps has emerged as a promising low-cost and scalable solution for autonomous driving. However, existing regression-based approaches often overlook inherent geometric priors, resulting in suboptimal training efficiency and limited localization accuracy. In this paper, we propose a novel homography-guided pose estimator network for fine-grained visual localization between multi-view images and standard-definition (SD) maps. We construct input pairs that satisfy a homography constraint by projecting ground-view features into the BEV domain and enforcing semantic alignment with map features. Then we leverage homography relationships to guide feature fusion and restrict the pose outputs to a valid feasible region, which significantly improves training efficiency and localization accuracy compared to prior methods relying on attention-based fusion and direct 3-DoF pose regression. To the best of our knowledge, this is the first work to unify BEV semantic reasoning with homography learning for image-to-map localization. Furthermore, by explicitly modeling homography transformations, the proposed framework naturally supports cross-resolution inputs, enhancing model flexibility. Extensive experiments on the nuScenes dataset demonstrate that our approach significantly outperforms existing state-of-the-art visual localization methods. Code and pretrained models will be publicly released to foster future research.

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Xuchang Zhong, Xu Cao, Jinke Feng, Hao Fang. 2026-01-06. HOLO: Homography-Guided Pose Estimator Network for Fine-Grained Visual Localization on SD Maps. https://arxiv.org/abs/2601.02730

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