vaishwarya96 / cross-season-segmentation

Code for "A Cross-Season Correspondence Dataset for Robust Semantic Segmentation"

A Cross-Season Correspondence Dataset for Robust Semantic Segmentation

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

Resources

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

Trained Models

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

Installation

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

Usage

• 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
• 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

Reference

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).

@article{larsson2019corr,
  title={A Cross-Season Correspondence Dataset for Robust Semantic Segmentation},
  author={Larsson, M{\aa}ns and Stenborg, Erik and Hammarstrand, Lars and Sattler, Torsten and Pollefeys, Mark and Kahl, Fredrik},
  journal={arXiv preprint arXiv:1903.06916},
  year={2019}
}

Other

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