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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}
}