InCaRPose is a Transformer-based model trained on synthetic data that predicts absolute metric-scale relative poses between distorted in-cabin camera views and generalizes to real images while releasing a new test dataset.
Robust im- age retrieval-based visual localization using kapture
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FastForward represents scenes as collections of 3D-anchored image features and performs camera pose estimation via feed-forward correspondence prediction, achieving competitive accuracy with minimal mapping time.
SplitGS-Loc disambiguates 2D-3D correspondences in photometrically optimized GSFFs via Mixture-of-Gaussians splitting and multi-view consistency selection, yielding stable PnP and SOTA localization results.
Sphere clouds neutralize density attacks on private 3D maps for visual localization while depth guidance from ToF sensors restores translation scale for accurate pose estimation.
The paper reformulates absolute pose regression as regressing disentangled world-coordinate raymaps and pointmaps from images, then recovering pose via a differentiable solver, claiming SOTA results on 7-Scenes and Cambridge Landmarks.
citing papers explorer
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InCaRPose: In-Cabin Relative Camera Pose Estimation Model and Dataset
InCaRPose is a Transformer-based model trained on synthetic data that predicts absolute metric-scale relative poses between distorted in-cabin camera views and generalizes to real images while releasing a new test dataset.
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A Scene is Worth a Thousand Features: Feed-Forward Camera Localization from a Collection of Image Features
FastForward represents scenes as collections of 3D-anchored image features and performs camera pose estimation via feed-forward correspondence prediction, achieving competitive accuracy with minimal mapping time.
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Disambiguating 2D-3D Correspondences in Gaussian Splatting-based Feature Fields for Visual Localization
SplitGS-Loc disambiguates 2D-3D correspondences in photometrically optimized GSFFs via Mixture-of-Gaussians splitting and multi-view consistency selection, yielding stable PnP and SOTA localization results.
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Depth-Guided Privacy-Preserving Visual Localization Using 3D Sphere Clouds
Sphere clouds neutralize density attacks on private 3D maps for visual localization while depth guidance from ToF sensors restores translation scale for accurate pose estimation.
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GRLoc: Geometric Representation Regression for Visual Localization
The paper reformulates absolute pose regression as regressing disentangled world-coordinate raymaps and pointmaps from images, then recovering pose via a differentiable solver, claiming SOTA results on 7-Scenes and Cambridge Landmarks.