Code accompanying the paper: AutoDispNet: Improving Disparity Estimation with AutoML (ICCV 2019). Parts of this codebase is inspired from DARTS.

Note: We provide deployment code only.

• tf 1.12
• Compile and install lmbspecialops.
• Install netdef_slim.
• Clone this repository.

• Change your directory to the network directory (autodispnet/nets).

• Download pre-trained weights with download_weights.sh. Pre-trained weights are provided for networks trained on FlyingThings (CSS, css) and fine-tuned on KITTI (CSS-KITTI). css is a network with smaller memory footprint (see paper for details).

• Go to a network directory (Eg: autodispnet/nets/CSS) and 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).

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        = "October",
  year         = "2019",
  url          = "http://lmb.informatik.uni-freiburg.de/Publications/2019/SMB19"
}

Tonmoy Saikia ([email protected])