wps1215/epi-shift

Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift

EPI-Shift

Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift

Requirements

• Python 3.6
• CUDA 9.2
• cuDNN 7.6
• HCI 4D Light Field Dataset

Installation

For the installation I recommend a python venv.

git clone git@github.com:titus-leistner/epi-shift.git
cd epi-shift/
python3 -m venv .
source bin/activate
pip install -r requirements.txt

Usage

To train the model, run

python train.py --tset path/to/hci4d/dataset/additional --vset path/to/hci4d/dataset/training --bsz [batch size]

Run python train.py --help to see all options.

To perform inference on a dataset, run

python infer.py --dset path/to/hci4d/dataset/test --prms path/to/params.pt

The disparity maps are saved as png and pfm files to ./out. Please download params.pt seperately.

Paper and Citation

The paper was published at 3DV 2019 with an oral presentation (project page).

@inproceedings{Leistner2019,
  title = {Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift},
  author = {Leistner, Titus and Schilling, Hendrik and Mackowiak, Radek and Gumhold, Stefan and Rother, Carsten},
  booktitle = {International Conference on 3D Vision (3DV)},
  doi = {10.1109/3DV.2019.00036},
  month = {sep},
  year = {2019},
}