PASSRnet: Parallax Attention Stereo Super-Resolution Network

Pytorch implementation of "Learning Parallax Attention for Stereo Image Super-Resolution", CVPR 2019

[arXiv] [CVF] [Supp]

Overview

Figure 1. Overview of our PASSRnet network.

Figure 2. Illustration of our parallax-attention mechanism.

Figure 3. A toy example illustration of the parallax-attention and cycle-attention maps generated by our PAM. The attention maps (30×30) correspond to the regions (1×30) marked by a yellow stroke. In (a) and (b), the first row represents left/right stereo images, the second row stands for parallax-attention maps, and the last row represents cycle-attention maps.

Flickr1024 Dataset

Figure 4. The Flickr1024 dataset.

Requirements

• pytorch (0.4), torchvision (0.2) (Note: The code is tested with

  python=3.6, cuda=9.0

  )
• Matlab (For training/test data generation)

Train

Prepare training data

1. Download the Flickr1024 dataset and put the images in

   data/train/Flickr1024

   (Note: In our paper, we also use 60 images in the Middlebury dataset as the training set.)
2. Cd to

   data/train

   and run

   generate_trainset.m

   to generate training data.

Begin to train

python train.py --scale_factor 4 --device cuda:0 --batch_size 32 --n_epochs 80 --n_steps 30

Test

Prepare test data

1. Download the KITTI2012 dataset and put folders

   testing/colored_0

   and

   testing/colored_1

   in

   data/test/KITTI2012/original

2. Cd to

   data/test

   and run

   generate_testset.m

   to generate test data.
3. (optional) You can also download KITTI2015, Middlebury or other stereo datasets and prepare test data in

   data/test

   as below:

   data
   └── test
     ├── dataset_1
           ├── hr
               ├── scene_1
                     ├── hr0.png
                     └── hr1.png
               ├── ...
               └── scene_M
           └── lr_x4
               ├── scene_1
                     ├── lr0.png
                     └── lr1.png
               ├── ...
               └── scene_M
     ├── ...
     └── dataset_N
   

Demo

python demo_test.py --scale_factor 4 --device cuda:0 --dataset KITTI2012

Results

Figure 5. Visual comparison for 2× SR. These results are achieved on “testimage013” of the KITTI 2012 dataset and “testimage019” of the KITTI 2015 dataset.

Figure 6. Visual comparison for 4× SR. These results are achieved on “testimage004” of the KITTI 2015 dataset.

Figure 7. Visual comparison for 2× SR. These results are achieved on a stereo image pair acquired in our laboratory.

Citation

@InProceedings{Wang2019Learning,
  author    = {Longguang Wang and Yingqian Wang and Zhengfa Liang and Zaiping Lin and Jungang Yang and Wei An and Yulan Guo},
  title     = {Learning Parallax Attention for Stereo Image Super-Resolution},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2019},
}

Contact

For questions, please send an email to [email protected]