Cross-View Image-to-Image Translation
8 papers with code • 8 benchmarks • 4 datasets
Most implemented papers
Image-to-Image Translation with Conditional Adversarial Networks
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.
Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation
In this paper, we propose a novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that makes it possible to generate images of natural scenes in arbitrary viewpoints, based on an image of the scene and a novel semantic map.
Cross-view image synthesis using geometry-guided conditional GANs
For this, we propose to use homography as a guide to map the images between the views based on the common field of view to preserve the details in the input image.
A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View
Accurate environment perception is essential for automated driving.
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.
Cross-View Image Matching for Geo-localization in Urban Environments
Next, for each building in the query image, we retrieve the $k$ nearest neighbors from the reference buildings using a Siamese network trained on both positive matching image pairs and negative pairs.
Cross-View Image Synthesis using Conditional GANs
X-Fork architecture has a single discriminator and a single generator.
Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs
This paper aims to develop an accurate 3D geometry representation of satellite images using satellite-ground image pairs.