README.md

AutoDispNet

Code for AutoDispNet: Improving Disparity Estimation with AutoML (ICCV 2019).

Note: We provide code for deployment only.

Setup

• Compile and install lmbspecialops
• Install netdef_slim
• Clone this repository.

Running networks

• Change your directory to a network directory (Eg: autodispnet/nets/CSS)
• Download pre-trained with with download_weights.sh
• Use the following command to test the network on an image pair: python3 controller.py eval image0_path image1_path out_dir
• The output is stored in a binary format with .float3 extension (Information on reading the output is here).

Citation

If you use the code or parts of it in your research, you should cite the aforementioned paper:

@InProceedings{SMB19,
  author       = "T. Saikia and Y. Marrakchi and A. Zela and F. Hutter and T. Brox",
  title        = "AutoDispNet: Improving Disparity Estimation With AutoML",
  booktitle    = "IEEE International Conference on Computer Vision (ICCV)",
  month        = " ",
  year         = "2019",
  url          = "http://lmb.informatik.uni-freiburg.de/Publications/2019/SMB19"
}

Author

Tonmoy Saikia ([email protected])