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

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

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) \