Camera Relocalization
15 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
Latest papers
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.
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.
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.
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.
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.
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.
Direct-PoseNet: Absolute Pose Regression with Photometric Consistency
ActiveVisionLab/direct-posenet
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We present a relocalization pipeline, which combines an absolute pose regression (APR) network with a novel view synthesis based direct matching module, offering superior accuracy while maintaining low inference time.
Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation
For the former we contributed our own dataset composed of five indoor scenes where it is unavoidable to capture images corresponding to views that are hard to uniquely identify.
Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments
siyandong/NeuralRouting
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CVPR 2021
Localizing the camera in a known indoor environment is a key building block for scene mapping, robot navigation, AR, etc.
Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes
In this paper, we adapt 3RScan - a recently introduced indoor RGB-D dataset designed for object instance re-localization - to create RIO10, a new long-term camera re-localization benchmark focused on indoor scenes.
