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

• URL: http://arxiv.org/abs/2601.02730v2
• Date: Wed, 07 Jan 2026 11:45:43 GMT
• Title: HOLO: Homography-Guided Pose Estimator Network for Fine-Grained Visual Localization on SD Maps
• Abstract summary: We propose a novel homography-guided pose estimator network for fine-grained visual localization between multi-view images and standard-definition (SD) maps.<n>To the best of our knowledge, this is the first work to unify BEV semantic reasoning with homography learning for image-to-map localization.
• Score: 13.643067075619841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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