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Found 6 papers, 3 papers with code
Learning Geo-Temporal Image Features
We propose to implicitly learn to extract geo-temporal image features, which are mid-level features related to when and where an image was captured, by explicitly optimizing for a set of location and time estimation tasks.
Learning to Look around Objects for Top-View Representations of Outdoor Scenes
Given a single RGB image of a complex outdoor road scene in the perspective view, we address the novel problem of estimating an occlusion-reasoned semantic scene layout in the top-view.
A Unified Model for Near and Remote Sensing
To evaluate our approach, we created a large dataset of overhead and ground-level images from a major urban area with three sets of labels: land use, building function, and building age.
Predicting Ground-Level Scene Layout from Aerial Imagery
We use our network to address the task of estimating the geolocation and geoorientation of a ground image.
Ranked #6 on
Cross-View Image-to-Image Translation
on cvusa
Detecting Vanishing Points using Global Image Context in a Non-Manhattan World
Our method reverses this process: we propose a set of horizon line candidates and score each based on the vanishing points it contains.
Ranked #2 on
Horizon Line Estimation
on York Urban Dataset
Horizon Lines in the Wild
The horizon line is an important contextual attribute for a wide variety of image understanding tasks.
Ranked #2 on
Horizon Line Estimation
on Horizon Lines in the Wild
