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Found 8 papers, 4 papers with code
CrowdDriven: A New Challenging Dataset for Outdoor Visual Localization
Visual localization is the problem of estimating the position and orientation from which a given image (or a sequence of images) is taken in a known 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.
Azimuthal Rotational Equivariance in Spherical CNNs
In this work, we analyze linear operators from $L^2(S^2) \rightarrow L^2(S^2)$ which are equivariant to azimuthal rotations, that is, rotations around the z-axis.
Single-Image Depth Prediction Makes Feature Matching Easier
Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines.
Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization
In this paper, we propose a new neural network, the Fine-Grained Segmentation Network (FGSN), that can be used to provide image segmentations with a larger number of labels and can be trained in a self-supervised fashion.
Semantic Match Consistency for Long-Term Visual Localization
Robust and accurate visual localization across large appearance variations due to changes in time of day, seasons, or changes of the environment is a challenging problem which is of importance to application areas such as navigation of autonomous robots.
Long-term Visual Localization using Semantically Segmented Images
Robust cross-seasonal localization is one of the major challenges in long-term visual navigation of autonomous vehicles.
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds.
