EDLSM

This is a Pytorch implementation for stereo matching described in the paper: Efficient Deep learning for stereo matching

Sample Output

Prerequisites

1. Pytorch Version --v0.4 with CUDA > 8.0
2. Numpy --v1.14
3. OpenCV --v3.2
4. Matplotlib --v2.1

Preparing training and validation data

First we need to generate the valid pixel locations.

1. Download the KITTI 2015 dataset.
2. Go to the cloned EDLSM folder and run the following command:

mkdir dataset

python prepare_kitti_dataset.py  --dataset_dir=/path/to/data_scene_flow/training --dump_root=./dataset

Note: In order to see other changeable parameters such as patch size, image height/width, train/val split etc, run the following command:

python prepare_kitti_dataset.py  --h

Training

Once the data is successfully prepared, the model can be trained by running the following command:

python train.py --dataset_dir=/path/to/data_scene_flow/training --train_dataset_name=tr_160_18_100.txt --checkpoint_dir=/where/to/store/checkpoints

Note: In order to see other changeable parameters such as batch size, image height/width, pixel weights, etc., run the following command:

python prepare_kitti_dataset.py  --h

In order to see the training loss graph open a tensorboard session by

tensorboard --logdir=./logs --port 8080

Inference

Once model is trained, we can generate disparity map by running the following command:

python inference_match.py --dataset_dir=/path/to/data_scene_flow/training --checkpoint_dir=/where/checkpoints/stored --checkpoint_file=eldsm_38000 --test_num=82

Note: In order to see other changeable parameters such as image height/width,disparity range, etc, run the following command:

python inference_match.py  --h

Future Work

Implement the post-processing steps.

Code citation

Original Code: https://bitbucket.org/saakuraa/cvpr16_stereo_public

Pytorch Code: https://github.com/vijaykumar01/stereo_matching

Paper citation

@inproceedings{luo2016efficient,
  title={Efficient deep learning for stereo matching},
  author={Luo, Wenjie and Schwing, Alexander G and Urtasun, Raquel},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5695--5703},
  year={2016}
}