Pytorch implementation of the several Deep Stereo Matching Network(DSMnet)
Contents
Introduction
Pytorch implementation of the several Deep Stereo Matching Network
Supported models:
Usage
Dependencies
Train
As an example, use the following command to train a DSM(such as DispnetCorr) on a dataset(such as KITTI2015)
./DSMnet_train_kitti.sh
As another example, use the following command to finetune a DSM(such as DispnetCorr) on a dataset(such as KITTI2015)
./DSMnet_finetune.sh
You need see the files(train_dispnetcorr_kitti.sh and train_dispnetcorr_kitti.sh) for details. You can alse change the DSM or dataset for train or finetune in the files.
submit
Use the following command to evaluate the trained DSM(such as DispnetCorr) on a dataset(such as KITTI2015 test data) without ground truth.
./DSMnet_submit.sh
You need see the file(submit_dispnetcorr_kitti.sh) for details.
Pretrained Model
KITTI | KITTI-raw-ss |
---|---|
DispnetC-pretrained-kitti | DispnetC-pretrained-kitti-raw-ss |
Results
Qualitative results
Left image
Right image
Predicted disparity
Licensing
Unless otherwise stated, the source code and trained Torch and Python model files are copyright Carnegie Mellon University and licensed under the Apache 2.0 License. Portions from the following third party sources have been modified and are included in this repository. These portions are noted in the source files and are copyright their respective authors with the licenses listed.