Camera Localization
38 papers with code • 2 benchmarks • 4 datasets
Latest papers
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.
Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras
One of the biggest challenges in parallel tracking and mapping with a monocular camera is to keep the scale consistent when reconstructing the geometric primitives.
Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image
This paper addresses the problem of vehicle-mounted camera localization by matching a ground-level image with an overhead-view satellite map.
Decoupling Makes Weakly Supervised Local Feature Better
Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences.
Continual Learning for Image-Based Camera Localization
For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component.
Learning Camera Localization via Dense Scene Matching
We present a new method for scene agnostic camera localization using dense scene matching (DSM), where a cost volume is constructed between a query image and a scene.
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms.
Towards Accurate Active Camera Localization
These approaches localize the camera in the discrete pose space and are agnostic to the localization-driven scene property, which restricts the camera pose accuracy in the coarse scale.
6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference
We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses.
Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences
With the pose prediction from VIO, we can efficiently obtain coarse 2D-3D line correspondences.