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Learning-Monocular-Depth-by-Stereo

This is the implementation of the paper Learning Monocular Depth by Distilling Cross-domain Stereo Networks, ECCV 18, Xiaoyang Guo, Hongsheng Li, Shuai Yi, Jimmy Ren, and Xiaogang Wang [Arxiv] [Demo]

Requirements

The code is implemented with Python 2.7 (Anaconda) (Python 3.x will cause bugs) and PyTorch 0.4.1.

conda install pytorch=0.4.1 torchvision -c pytorch
conda install opencv3 -c menpo
pip install tensorboardX
./scripts/install.sh  # install correlation1d_package and resample1d_package

Dataset Preparation

Scene Flow

DATASET_PATH=~/dataset/scene_flow  # or any other directory
mkdir -p $DATASET_PATH

# download finalpass frames and disparity
wget -i ./scripts/sceneflow_dataset_files.txt -P $DATASET_PATH
cd $DATASET_PATH

# extract finalpass frmaes
mkdir RGB_finalpass
tar -xvf flyingthings3d__frames_finalpass.tar --strip-components=1 -C RGB_finalpass
tar -xvf driving__frames_finalpass.tar --strip-components=1 -C RGB_finalpass/TRAIN
tar -xvf monkaa__frames_finalpass.tar --strip-components=1 -C RGB_finalpass/TRAIN

# extract disparity
tar -xvf flyingthings3d__disparity.tar.bz2
tar -xvf driving__disparity.tar.bz2 --strip-components=1 -C disparity/TRAIN
tar -xvf monkaa__disparity.tar.bz2 --strip-components=1 -C disparity/TRAIN

KITTI Eigen Split

DATASET_PATH=~/dataset/kitti_raw  # or any other directory
mkdir -p $DATASET_PATH
wget -i ./scripts/kitti_raw_dataset_files.txt -P $DATASET_PATH  # KITTI Raw (66 files)
cd $DATASET_PATH
unzip *.zip

Run the code

Training

# Step 1, pretrain the stereo model on Scene Flow datasets
./run_train_stereo_sceneflow.sh
# Step 2, supervised or unsupervised fine-tune on KITTI Eigen Split
./run_supft100_stereo_kitti.sh
./run_unsupft_stereo_kitti.sh
# Step 3, train the monocular depth model
./run_distill_mono.sh

Testing

# test stereo models on KITTI Eigen Split, please update TEST_WORK_DIR in the bash file.
./run_test_stereo_kitti.sh
# test monocular models on KITTI Eigen Split, please update TEST_WORK_DIR in the bash file.
./run_test_mono_kitti.sh

The evaluation code is from Monodepth. We use the same crop option --garg_crop to evaluate the model. NOTE that we use the depth from camera view instead of LIDAR view, which is different from the default option of Monodepth.

Pretrained Model

Stereo Model

StereoNoFt (Pretrained Stereo Model on Scene Flow datasets)

StereoUnsupFt

StereoSupFt100

Monocular Depth Model

StereoNoFt-->Mono

StereoUnsupFt-->Mono

StereoUnsupFt-->Mono pt

StereoUnsupFt-->Mono pt C,K

StereoSupFt100-->Mono

StereoSupFt100-->Mono pt

StereoSupFt100-->Mono pt C,K

TODO

  • Check training code related to Cityscapes dataset.

NOTE

  • By default use_pretrained_weights (use ImageNet pretrained weights) is enabled for monocular network.
  • The arguments of Correlation1d cannot be changed, or it may cause bugs.

Citation

If you find this code useful in your research, please cite:

@inproceedings{guo2018learning,
  title={Learning monocular depth by distilling cross-domain stereo networks},
  author={Guo, Xiaoyang and Li, Hongsheng and Yi, Shuai and Ren, Jimmy and Wang, Xiaogang},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={484--500},
  year={2018}
}

Acknowledgements

The evaluation code is from Monodepth. Unsupervised Monocular Depth Estimation with Left-Right Consistency, by C. Godard, O Mac Aodha, G. Brostow, CVPR 2017.

The correlation1d and resample1d modules are modified from correlation2d and resample2d modules in flownet2-pytorch.

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Learning Monocular Depth by Distilling Cross-domain Stereo Networks, ECCV18

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