A Scene Is Worth A Thousand Features: Feed-forward Camera Localization From A Collection Of Image Features

Abstract

Visually localizing an image, i.e., estimating its camera pose, requires building a scene representation that serves as a visual map. The representation we choose has direct consequences towards the practicability of our system. Even when starting from mapping images with known camera poses, state-of-the-art approaches still require hours of mapping time in the worst case, and several minutes in the best. This work raises the question whether we can achieve competitive accuracy much faster. We introduce FastForward, a method that creates a map representation and relocalizes a query image on-the-fly in a single feed-forward pass. At the core, we represent multiple mapping images as a collection of features anchored in 3D space. FastForward utilizes these mapping features to predict image-to-scene correspondences for the query image, enabling the estimation of its camera pose. We couple FastForward with image retrieval and achieve state-of-the-art accuracy when compared to other approach

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keybarrosolaguna2025a

Related papers

• Visible Structure Retrieval For Lightweight Image-based Relocalisation (2025)0.00
• Mapping, Localization And Path Planning For Image-based Navigation Using Visual Features And Map (2018)11.93
• Multiview Image-based Localization (2025)0.00
• Supergf: Unifying Local And Global Features For Visual Localization (2022)0.00
• Exploiting Local Indexing And Deep Feature Confidence Scores For Fast Image-to-video Search (2018)2.26
• Leveraging Local And Global Descriptors In Parallel To Search Correspondences For Visual Localization (2020)8.82
• Reuse Your Features: Unifying Retrieval And Feature-metric Alignment (2022)1.69
• Sparse-to-dense Hypercolumn Matching For Long-term Visual Localization (2019)12.99