This is an implementation of the work published in A Cross-Season Correspondence Dataset for Robust Semantic Segmentation (https://arxiv.org/abs/1903.06916)

The datasets used in the paper are available at visuallocalization.net

https://drive.google.com/open?id=14joxT0XFreW1WX3M8oTiCV69hZTiJTMV

A Dockerfile is provided, either build a docker image using this or refer to the requirements listed in the file.

• Download Cityscapes and Mapillary Vistas
• Use /utils/convert_vistas_to_cityscapes.py to create cityscapes class annotations for the Vistas images
• Download the correspondence datasets, https://www.visuallocalization.net/datasets/
• Download the images associated with the correspondence datasets (instructions available in dataset readme)
• Create a global_otps.json and set the paths (see global_opts_example.json)
• Train, see train/train_many.py for reproduction of paper experiments

If you use this code, please cite the following paper:

Måns Larsson, Erik Stenborg, Lars Hammarstrand, Torsten Sattler, Mark Pollefeys and Fredrik Kahl "A Cross-Season Correspondence Dataset for Robust Semantic Segmentation" Proc. CVPR (2019).

@InProceedings{Larsson_2019_CVPR,
author = {Larsson, Mans and Stenborg, Erik and Hammarstrand, Lars and Pollefeys, Marc and Sattler, Torsten and Kahl, Fredrik},
title = {A Cross-Season Correspondence Dataset for Robust Semantic Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

Some code from https://github.com/zijundeng/pytorch-semantic-segmentation and https://github.com/kazuto1011/pspnet-pytorch was used.