elnino9ykl / PASS

Panoramic Annular Semantic Segmentation

Panoramic Annular Semantic Segmentation

Panoramic Annular Semantic Segmentation in PyTorch

PASS Dataset

GoogleDrive

New! Google Drive Download Link

BaiduYun

For Validation (Most important files):

Unfolded Panoramas for Validation, (400 images)

Annonations, (400 annotation images)

Groundtruth

There are 400 panoramas with annotations. Please use the Annotations data for evaluation.

In total, there are 1050 panoramas. Complete Panoramas:

All Unfolded Panoramas

RAW Panoramas: RAW1, RAW2, RAW3

Packages

For instructions please refer to the README on each folder:

• train contains tools for training the network for semantic segmentation.
• eval contains tools for evaluating/visualizing the network's output for panoramic annular semantic segmentation.
• trained_models Contains the trained ERF-PSPNet and ERF-APSPNet models.

CODE Requirements:

• Dataset (I suggest using Mapillary Vistas or Cityscpaes as training datasets. For evaluation, semantic segmentation on PASS, VISTAS or Cityscapes datasets can be tested using this code.) The Dataset shoudld be structured as the dataset folder indicates.
• Python 3.6: If you don't have Python3.6 in your system, I suggest installing it with Anaconda.
• PyTorch: Make sure to install the Pytorch version for Python 3.6 with CUDA support (code tested for CUDA 8.0, CUDA 9.0 and CUDA 10.0). I am using PyTorch 0.4.1.
• Additional Python packages: numpy, matplotlib, Pillow, torchvision and visdom (optional for --visualize flag)

In Anaconda you can install with:

conda install numpy matplotlib torchvision Pillow
conda install -c conda-forge visdom

If you use Pip (make sure to have it configured for Python3.6) you can install with:

pip install numpy matplotlib torchvision Pillow visdom

Publications

If you use our code or dataset, please consider referencing any of the following papers:

PASS: Panoramic Annular Semantic Segmentation. K. Yang, X. Hu, L.M. Bergasa, E. Romera, K. Wang. IEEE Transactions on Intelligent Transportation Systems (T-ITS), September 2019. [PDF]

@article{yang2020pass,
title={PASS: Panoramic Annular Semantic Segmentation},
author={Yang, Kailun and Hu, Xinxin and Bergasa, Luis M and Romera, Eduardo and Wang, Kaiwei},
journal={IEEE Transactions on Intelligent Transportation Systems},
volume={21},
number={10},
pages={4171--4185},
year={2020},
publisher={IEEE}
}

Can we PASS beyond the Field of View? Panoramic Annular Semantic Segmentation for Real-World Surrounding Perception. K. Yang, X. Hu, L.M. Bergasa, E. Romera, X. Huang, D. Sun, K. Wang. In IEEE Intelligent Vehicles Symposium (IV), Paris, France, June 2019, pp. 446-453. [PDF] [VIDEO]

@inproceedings{yang2019can,
title={Can we pass beyond the field of view? panoramic annular semantic segmentation for real-world surrounding perception},
author={Yang, Kailun and Hu, Xinxin and Bergasa, Luis M and Romera, Eduardo and Huang, Xiao and Sun, Dongming and Wang, Kaiwei},
booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)},
pages={446--453},
year={2019},
organization={IEEE}
}