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Probabilistic Normalized Convolutional Neural Networks (pNCNN)

The official PyTorch implementation for "Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End" presented at CVPR 2020, Seattle, USA.

[PDF] [Supplementary] [Poster]

@article{eldesokey2018propagating,
  title={Propagating Confidences through CNNs for Sparse Data Regression},
  author={Eldesokey, Abdelrahman and Felsberg, Michael and Khan, Fahad Shahbaz},
  journal={arXiv preprint arXiv:1805.11913},
  year={2018}
}

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Dependecies

The code was tested with Python 3.7.4 and PyTorch 1.4, but it should work on any PyTorch version > 1.1

• pytorch>1.1
• json
• matplotlib
• opencv
• h5py

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Datasets

Kitti-Depth

To download the Kitti-Depth dataset, use the provided Python script dataloaders/download_kitti_depth_rgb.py.

Remeber to edit the script first to set download directories.

NYU-Depth-v2

Download and extract the dataset in h5 format provided from sparse-to-dense.

wget http://datasets.lids.mit.edu/sparse-to-dense/data/nyudepthv2.tar.gz
tar -xvf nyudepthv2.tar.gz && rm -f nyudepthv2.tar.gz