Camera Relocalization
16 papers with code • 0 benchmarks • 2 datasets
"Camera relocalization, or image-based localization is a fundamental problem in robotics and computer vision. It refers to the process of determining camera pose from the visual scene representation and it is essential for many applications such as navigation of autonomous vehicles, structure from motion (SfM), augmented reality (AR) and simultaneous localization and mapping (SLAM)." (Source)
Benchmarks
These leaderboards are used to track progress in Camera Relocalization
Cambridge Landmarks
DeepLoc
Most implemented papers
DFNet: Enhance Absolute Pose Regression with Direct Feature Matching
We introduce a camera relocalization pipeline that combines absolute pose regression (APR) and direct feature matching.
RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments
Experiments demonstrate that RobustLoc surpasses current state-of-the-art camera pose regression models and achieves robust performance in various environments.
Fast and Lightweight Scene Regressor for Camera Relocalization
ais-lab/feat2map
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The proposed approach uses sparse descriptors to regress the scene coordinates, instead of a dense RGB image.
D2S: Representing local descriptors and global scene coordinates for camera relocalization
ais-lab/feat2map
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In this study, we propose a direct learning-based approach that utilizes a simple network named D2S to represent local descriptors and their scene coordinates.
HR-APR: APR-agnostic Framework with Uncertainty Estimation and Hierarchical Refinement for Camera Relocalisation
lck666666/hr-apr
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In addition, we take advantage of the uncertainty for pose refinement to enhance the performance of APR.
Representing 3D sparse map points and lines for camera relocalization
Recent advancements in visual localization and mapping have demonstrated considerable success in integrating point and line features.
