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
Latest papers with no code
6D Dynamic Camera Relocalization From Single Reference Image
Based on inexpensive platform with unreliable absolute repositioning accuracy (ARA), we propose a hand-eye calibration free strategy to actively relocate camera into the same 6D pose that produces the input reference image, by sequentially correcting 3D relative rotation and translation.
Learning to Navigate the Energy Landscape
We demonstrate the efficacy of our approach on the challenging problem of RGB Camera Relocalization.
Exploiting Uncertainty in Regression Forests for Accurate Camera Relocalization
Recent advances in camera relocalization use predictions from a regression forest to guide the camera pose optimization procedure.
Multi-Output Learning for Camera Relocalization
We formulate this problem as inversion of the generative rendering procedure, i. e., we want to find the camera pose corresponding to a rendering of the 3D scene model that is most similar with the observed input.
Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
We address the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image.
