Paper page - CIPER: A Unified Framework for Cross-view Image-retrieval and Pose-estimation

Source: https://huggingface.co/papers/2606.05011

Abstract

CIPER is a unified cross-view geo-localization framework that simultaneously performs city-scale retrieval and precise 3-DoF pose estimation using a shared transformer encoder and two-way pose decoder.

Cross-view geo-localizationestimates the geographic location of a ground image by matching it against an aerial image database. Existing methods tackle this through eitherlarge-scale retrievalorprecise pose estimation, but not both: retrieval-based methods enable wide-area search at the cost of localization accuracy, while pose estimation methods achieve high precision within only a narrow search space. Naively cascading these pipelines introduces error propagation and inconsistent feature representations. We formulatecross-view geo-localizationas a unified problem requiring simultaneous city-scale retrieval and precise 3-DoF pose estimation. We propose CIPER (Cross-view Image-retrieval and Pose-estimation transformER), a single architecture that jointly performs both tasks through mutually beneficial feature learning. CIPER uses a sharedtransformer encoderwithtask-specific tokensto disentangle global retrieval features from spatial localization cues. To bridge the large domain gap between ground and aerial views, we introduce atwo-way transformer pose decoderthat uses ground features as spatial queries for bidirectionalcross-attention. Aset prediction strategyfurther enables stable 3-DoF regression under a unifiedmulti-task objective. Experiments on VIGOR, KITTI, and Ford Multi-AV demonstrate competitive performance, especially under limited field-of-view and arbitrary orientation conditions. Code is available at https://github.com/yurimjeon1892/CIPER.

View arXiv pageView PDFGitHub0Add to collection

Get this paper in your agent:

hf papers read 2606\.05011

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2606.05011 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2606.05011 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2606.05011 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.