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: >=6.5G (for testing);  >=11G (for training, >=22G is prefered)
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 (>=1.0) by "pip install" to run the code. See this feihuzhang/GANet#24

But, if you have trouble (lib conflicts) when compiling cuda libs, installing pytorch from source would help solve most of the errors (lib conflicts).

Please refer to https://github.com/pytorch/pytorch about how to reinstall pytorch from source.

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".
• If you met the BN error, try to replace the sync-bn with another version:
  1. Install NVIDIA-Apex package https://github.com/NVIDIA/apex $ git clone https://github.com/NVIDIA/apex $ cd apex $ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
  2. Revise the "GANet_deep.py": add import apex change all BatchNorm2d and BatchNorm3d to apex.parallel.SyncBatchNorm

Step 2: download and prepare the dataset

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 ${your dataset PATH}/disparity/TRAIN/
  -make sure the following 29 folders are included in the "${your dataset PATH}/disparity/TRAIN/" and "${your dataset PATH}/frames_finalpass/TRAIN/":
    
    15mm_focallength	35mm_focallength		A			 a_rain_of_stones_x2		B				C
    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 48 (for "DSMNet") or 64 (for "DSMNet2x2"), "max_disp" must be multiple of 12 (for "DSMNet") or 16 (for "DSMNet2x2") (default: 192).

Pretrained models:

Updating ...

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}
}