Visual Localization
151 papers with code • 5 benchmarks • 19 datasets
Visual Localization is the problem of estimating the camera pose of a given image relative to a visual representation of a known scene.
Benchmarks
These leaderboards are used to track progress in Visual Localization
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Libraries
Use these libraries to find Visual Localization models and implementationsDatasets
NCLT
Cambridge Landmarks
Aachen Day-Night
InLoc
KAIST Urban
DeepLoc
Long-term visual localization
PhotoSynth
Latest papers with no code
PRAM: Place Recognition Anywhere Model for Efficient Visual Localization
Humans localize themselves efficiently in known environments by first recognizing landmarks defined on certain objects and their spatial relationships, and then verifying the location by aligning detailed structures of recognized objects with those in the memory.
3DGS-ReLoc: 3D Gaussian Splatting for Map Representation and Visual ReLocalization
This paper presents a novel system designed for 3D mapping and visual relocalization using 3D Gaussian Splatting.
The NeRFect Match: Exploring NeRF Features for Visual Localization
Significantly, we introduce NeRFMatch, an advanced 2D-3D matching function that capitalizes on the internal knowledge of NeRF learned via view synthesis.
Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency
After expanding the training set, we propose a training approach that leverages the specificities and the underlying geometry of this mix of real and synthetic images.
Semantic Object-level Modeling for Robust Visual Camera Relocalization
Due to the improvement of CNN-based object detection algorithm, the robustness of visual relocalization is greatly enhanced especially in viewpoints where classical methods fail.
UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization
Despite significant progress in global localization of Unmanned Aerial Vehicles (UAVs) in GPS-denied environments, existing methods remain constrained by the availability of datasets.
LIP-Loc: LiDAR Image Pretraining for Cross-Modal Localization
We apply this approach to the domains of 2D image and 3D LiDAR points on the task of cross-modal localization.
PNeRFLoc: Visual Localization with Point-based Neural Radiance Fields
In this paper, we propose a novel visual localization framework, \ie, PNeRFLoc, based on a unified point-based representation.
Implicit Learning of Scene Geometry from Poses for Global Localization
In this paper, we propose to utilize these minimal available labels (. i. e, poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose.
G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-training
G2D achieves superior performance across 6 medical imaging tasks and 25 diseases, particularly in semantic segmentation, which necessitates fine-grained, semantically-grounded image features.
