Cross-View Image-to-Image Translation

8 papers with code • 8 benchmarks • 4 datasets

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Benchmarks

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These leaderboards are used to track progress in Cross-View Image-to-Image Translation

Dataset	Best Model	Compare
cvusa	UniGAN	See all
Dayton (256×256) - aerial-to-ground	SelectionGAN	See all
Dayton (64×64) - aerial-to-ground	SelectionGAN	See all
Dayton (64x64) - ground-to-aerial	SelectionGAN	See all
Dayton (256×256) - ground-to-aerial	SelectionGAN	See all
Ego2Top	SelectionGAN	See all
Cam2BEV	uNetXST	See all
CVACT	Sat2Density	See all

Datasets

Most implemented papers

Most implemented Social Latest No code

Image-to-Image Translation with Conditional Adversarial Networks

phillipi/pix2pix • • CVPR 2017

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.

175

Paper
Code

Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

Ha0Tang/SelectionGAN • • CVPR 2019

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.

Paper
Code

Cross-view image synthesis using geometry-guided conditional GANs

kregmi/cross-view-image-synthesis • • 14 Aug 2018

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.

Paper
Code

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

ika-rwth-aachen/Cam2BEV • • 8 May 2020

Accurate environment perception is essential for automated driving.

Paper
Code

Predicting Ground-Level Scene Layout from Aerial Imagery

viibridges/crossnet • • CVPR 2017

We use our network to address the task of estimating the geolocation and geoorientation of a ground image.

Paper
Code

Cross-View Image Matching for Geo-localization in Urban Environments

viibridges/crossnet • • CVPR 2017

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.

Paper
Code