Multiview Image-based Localization

Abstract

The image retrieval (IR) approach to image localization has distinct advantages to the 3D and the deep learning (DNN) approaches: it is seen-agnostic, simpler to implement and use, has no privacy issues, and is computationally efficient. The main drawback of this approach is relatively poor localization in both position and orientation of the query camera when compared to the competing approaches. This paper represents a hybrid approach that stores only image features in the database like some IR methods, but relies on a latent 3D reconstruction, like 3D methods but without retaining a 3D scene reconstruction. The approach is based on two ideas: \{\em (i)\} a novel proposal where query camera center estimation relies only on relative translation estimates but not relative rotation estimates through a decoupling of the two, and \{\em (ii)\} a shift from computing optimal pose from estimated relative pose to computing optimal pose from multiview correspondences, thus cutting out the ``mi

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyfiore2025multiview

Related papers

• Benchmarking Image Retrieval For Visual Localization (2020)17.78
• Investigating The Role Of Image Retrieval For Visual Localization -- An Exhaustive Benchmark (2022)16.58
• Location Field Descriptors: Single Image 3D Model Retrieval In The Wild (2019)11.39
• Localizing And Orienting Street Views Using Overhead Imagery (2016)17.26
• Visible Structure Retrieval For Lightweight Image-based Relocalisation (2025)0.00
• Leveraging Local And Global Descriptors In Parallel To Search Correspondences For Visual Localization (2020)8.82
• Clnet: Cross-view Correspondence Makes A Stronger Geo-localizationer (2025)0.00
• Cross-view Image Retrieval -- Ground To Aerial Image Retrieval Through Deep Learning (2020)5.24