DSMNet

Domain-invariant Stereo Matching Newtorks

Oral Presentation

Slides, Video

Great Generalization Abilities:

DSMNet has great generalization abilities on other datasets/scenes. Models are trained only with synthetic data:

DATASET

Carla Dataset: updating ...

Building Requirements:

gcc: >=5.3
GPU mem: >=5G (for testing);  >=11G (for training)
pytorch: >=1.0
cuda: >=9.2 (9.0 doesn’t support well for the new pytorch version and may have “pybind11 errors”.)
tested platform/settings:
  1) ubuntu 16.04 + cuda 10.0 + python 3.6, 3.7
  2) centos + cuda 9.2 + python 3.7

Install Pytorch:

You can easily install pytorch (>=1.1) by "pip install" or anaconda.

How to Use?

Step 1: compile the libs by "sh compile.sh"

• Change the environmental variable ($PATH, $LD_LIBRARY_PATH etc.), if it's not set correctly in your system environment (e.g. .bashrc). Examples are included in "compile.sh".

Step 2: download and prepare the training dataset or your own testing set.

download SceneFLow dataset: "FlyingThings3D", "Driving" and "Monkaa" (final pass and disparity files).

  -mv all training images (totallty 29 folders) into ${your dataset PATH}/frames_finalpass/TRAIN/
  -mv all corresponding disparity files (totallty 29 folders) into ${your dataset PATH}/disparity/TRAIN/
  -make sure the following 27 folders are included in the "${your dataset PATH}/disparity" "${your dataset PATH}/frames_cleanpass" and "${your dataset PATH}/frames_finalpass":

    15mm_focallength    35mm_focallength        TRAIN            a_rain_of_stones_x2                        
    eating_camera2_x2   eating_naked_camera2_x2     eating_x2        family_x2          flower_storm_augmented0_x2  flower_storm_augmented1_x2
    flower_storm_x2 funnyworld_augmented0_x2    funnyworld_augmented1_x2    funnyworld_camera2_augmented0_x2    funnyworld_camera2_augmented1_x2    funnyworld_camera2_x2
    funnyworld_x2   lonetree_augmented0_x2      lonetree_augmented1_x2      lonetree_difftex2_x2          lonetree_difftex_x2       lonetree_winter_x2
    lonetree_x2     top_view_x2         treeflight_augmented0_x2    treeflight_augmented1_x2    treeflight_x2   

download and extract Carla, kitti and kitti2015 datasets.

Step 3: revise parameter settings and run "train.sh" and "predict.sh" for training, finetuning and prediction/testing. Note that the “crop_width” and “crop_height” must be multiple of 64 (for "DSMNet2x2"), "max_disp" must be multiple of 16 (for "DSMNet2x2") (default: 192).

Pretrained models:

These pre-trained models perform a little better than those reported in the paper. If you want to compute disparity maps on your new stereo images, "Mixed (Real + Synthetic)" would be the best choice.

Reference:

If you find the code useful, please cite our paper:

@inproceedings{zhang2019domaininvariant,
  title={Domain-invariant Stereo Matching Networks},
  author={Feihu Zhang and Xiaojuan Qi and Ruigang Yang and Victor Prisacariu and Benjamin Wah and Philip Torr},
  booktitle={Europe Conference on Computer Vision (ECCV)},
  year={2020}
}

@inproceedings{Zhang2019GANet,
  title={GA-Net: Guided Aggregation Net for End-to-end Stereo Matching},
  author={Zhang, Feihu and Prisacariu, Victor and Yang, Ruigang and Torr, Philip HS},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={185--194},
  year={2019}
}