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Please cite the paper below if you find our paper and source codes are useful.
@inproceedings{Hur:2019:IRR,
Author = {Junhwa Hur and Stefan Roth},
Booktitle = {CVPR},
Title = {Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation},
Year = {2019}
}
Contact: junhwa.hur[at]visinf.tu-darmstadt.de
Getting started
This code has been orginally developed under Anaconda(Python 3.6), PyTorch 0.4.1 and CUDA 8.0 on Ubuntu 16.04.
Please install the followings:
Anaconda
PyTorch (now compatible with PyTorch 1.5.0)
tqdm (conda install -c conda-forge tqdm==4.40.0)
(any missing packages that the code requires)
The datasets used for this projects are followings:
The scripts folder contains training scripts of experiments demonstrated in the paper.
To train the model, you can simply run the script file, e.g., ./IRR-PWC_flyingChairsOcc.sh.
In script files, please configure your own experiment directory (EXPERIMENTS_HOME) and dataset directory in your local system (e.g., SINTEL_HOME or KITTI_HOME).
Pretrained Models
The saved_check_point contains the pretrained models of i) baseline, ii) baseline + irr, and iii) full models.
Additional pretrained models in the ablations study (Table 1 in the main paper) and their training scripts are available upon request.
Inference
The scripts for testing the pre-trained models are located in scripts/validation.
Acknowledgement
Portions of the source code (e.g., training pipeline, runtime, argument parser, and logger) are from Jochen Gast
About
Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation (CVPR 2019)