Pyramid Stereo Matching Network

This repository contains the code (in PyTorch) for "Pyramid Stereo Matching Network" paper (CVPR 2018) by Jia-Ren Chang and Yong-Sheng Chen.

Citation

@article{chang2018pyramid,
  title={Pyramid Stereo Matching Network},
  author={Chang, Jia-Ren and Chen, Yong-Sheng},
  journal={arXiv preprint arXiv:1803.08669},
  year={2018}
}

Introduction
Usage
Results
Contacts

Introduction

Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs). However, current architectures rely on patch-based Siamese networks, lacking the means to exploit context information for finding correspondence in illposed regions. To tackle this problem, we propose PSMNet, a pyramid stereo matching network consisting of two main modules: spatial pyramid pooling and 3D CNN. The spatial pyramid pooling module takes advantage of the capacity of global context information by aggregating context in different scales and locations to form a cost volume. The 3D CNN learns to regularize cost volume using stacked multiple hourglass networks in conjunction with intermediate supervision.

Usage

Dependencies

Python2.7
PyTorch(0.3.0+)
torchvision 0.2.0 (higher version may cause issues)
KITTI Stereo
Scene Flow

Usage of Scene Flow dataset
Download RGB cleanpass images and its disparity for three subset: FlyingThings3D, Driving, and Monkaa.
Put them in the same folder.
And rename the folder as: "driving_frames_cleanpass", "driving_disparity", "monkaa_frames_cleanpass", "monkaa_disparity", "frames_cleanpass", "frames_disparity".

Train

As an example, use the following command to train a PSMNet on Scene Flow

python main.py --maxdisp 192 \
               --model stackhourglass \
               --datapath (your scene flow data folder)\
               --epochs 10 \
               --loadmodel  (optional)\
               --savemodel (path for saving model)

As another example, use the following command to finetune a PSMNet on KITTI 2015

python finetune.py --maxdisp 192 \
                   --model stackhourglass \
                   --datatype 2015 \
                   --datapath (KITTI 2015 training data folder) \
                   --epochs 300 \
                   --loadmodel (pretrained PSMNet) \
                   --savemodel (path for saving model)

You can alse see those example in run.sh

Evaluation

Use the following command to evaluate the trained PSMNet on KITTI 2015 test data

python submission.py --maxdisp 192 \
                     --model stackhourglass \
                     --KITTI 2015 \
                     --datapath (KITTI 2015 test data folder) \
                     --loadmodel (finetuned PSMNet) \