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DeepPose

A general Riemannian formulation of the pose estimation problem to train CNNs directly on SE(3) equipped with a left-invariant Riemannian metric.

Getting Started

Prerequisites

This loss function is created for TensorFlow (r1.2+) and PyTorch (v0.1+). A modified Caffe distribution of the implementation can be found here.

This package requires Geomstats (v1.5+) and its prerequisites.

Usage

Git clone this repository and copy se3_geodesic_loss.py to your neural network project folder. Import it into your python project using:

from se3_geodesic_loss import SE3GeodesicLoss

Create the loss object with desired loss weights, and invoke it as part of your computational graph.

weight = np.array([1., 1., 1., 1., 1., 1.])

# TensorFlow
loss = SE3GeodesicLoss(weight)
geodesic_loss = loss.geodesic_loss(y_pred, y_true)

# PyTorch
loss = SE3GeodesicLoss(weight)(y_pred, y_true)
loss.backward()

y_pred and y_true input tensors must have shape [Nx6]. The gradient is calculated w.r.t. y_pred only.

Authors & Citation

  • Benjamin Hou
  • Nina Miolane
  • Bishesh Khanal
  • Bernhard Kainz

If you like our work and found it useful for your research, please cite our paper. Thanks! :)

@inproceedings{hou2018computing,
  title={Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry},
  author={Hou, Benjamin and Miolane, Nina and Khanal, Bishesh and Lee, Matthew and Alansary, Amir and McDonagh, Steven and Hajnal, Jo V and Rueckert, Daniel and Glocker, Ben and Kainz, Bernhard},
  booktitle={ International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2018},
  organization={Springer}
}
@misc{miolane2018geomstats, 
  title={Geomstats: Computations and Statistics on Manifolds with Geometric Structures.}, 
  url={https://github.com/ninamiolane/geomstats}, 
  journal={GitHub}, 
  author={Miolane, Nina and Mathe, Johan and Pennec, Xavier}, 
  year={2018}, 
  month={Feb}
}

License

This project is licensed under the MIT License - see the LICENSE.md file for details

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