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Found 5 papers, 2 papers with code
C-BEV: Contrastive Bird's Eye View Training for Cross-View Image Retrieval and 3-DoF Pose Estimation
To find the geolocation of a street-view image, cross-view geolocalization (CVGL) methods typically perform image retrieval on a database of georeferenced aerial images and determine the location from the visually most similar match.
Geo-Tiles for Semantic Segmentation of Earth Observation Imagery
This allows us to perform a tile specific data augmentation during training and a substitution of pixel predictions with limited context information using data of overlapping tiles during inference.
Uncertainty-aware Vision-based Metric Cross-view Geolocalization
This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial image to determine the vehicle's geo-pose.
Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors
Our method is the first to utilize on-board cameras in an end-to-end differentiable model for metric self-localization on unseen orthophotos.
Improving Semantic Image Segmentation via Label Fusion in Semantically Textured Meshes
Models for semantic segmentation require a large amount of hand-labeled training data which is costly and time-consuming to produce.
