DeepPose-caffe

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

Build and Installation

This package requires building Caffe with Intel MKL.

Modified Files

• cmake/Summary.cmake

• cmake/Dependencies.cmake

• cmake/Modules/FindMKL.cmake

• include/caffe/layers/base_data_layer.hpp

• src/caffe/layers/data_layer.cpp

• src/caffe/layers/dropout_layer.cpp

• src/caffe/layers/dropout_layer.cu

• src/caffe/proto/caffe.proto

Added Files

• include/caffe/layers/normalize_layer.hpp

• include/caffe/layers/se3_geodesic_loss_layer.hpp

• include/caffe/layers/so3_quaternion_loss2.hpp

• include/caffe/layers/so3_quaternion_loss3.hpp

• include/caffe/layers/so3_quaternion_loss4.hpp

• src/caffe/layers/normalize_layer.cpp

• src/caffe/layers/normalize_layer.cu

• src/caffe/layers/se3_geodesic_loss_layer.cpp

• src/caffe/layers/se3_geodesic_loss_layer.cu

• src/caffe/layers/so3_quaternion_loss2.cpp

• src/caffe/layers/so3_quaternion_loss2.cu

• src/caffe/layers/so3_quaternion_loss3.cpp

• src/caffe/layers/so3_quaternion_loss3.cu

• src/caffe/layers/so3_quaternion_loss4.cpp

• src/caffe/layers/so3_quaternion_loss4.cu

• src/caffe/test/test_normalize_layer.cu

• src/caffe/test/test_se3_geodesic_loss_layer.cpp

• src/caffe/test/test_so3_quaternion_loss2.cpp

• src/caffe/test/test_so3_quaternion_loss3.cpp

• src/caffe/test/test_so3_quaternion_loss4.cpp

Added Layers

These loss functions optimises on the manifold

• SE3 Geodesic Loss (Rotation + Translation)

• SO3 Quaternion Loss (Rotations only)

• Instance Normalisation Layer

Usage

See DeepPose/README.md

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}
}

Acknowledgements

• Miolane et al. Geomstats

• Kendall et al. - Kings College Dataset

• Du Q. Hyunh et al. - Metrics for 3D Rotations: Comparison and Analysis

Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

Check out the project site for all the details like

• DIY Deep Learning for Vision with Caffe

• Tutorial Documentation

• BAIR reference models and the community model zoo

• Installation instructions

and step-by-step examples.

Custom distributions

• Intel Caffe (Optimized for CPU and support for multi-node), in particular Xeon processors (HSW, BDW, SKX, Xeon Phi).

• OpenCL Caffe e.g. for AMD or Intel devices.

• Windows Caffe

Community

Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.

Happy brewing!

License and Citation

Caffe is released under the BSD 2-Clause license. The BAIR/BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}