Pure Numpy Implementation of the Coherent Point Drift Algorithm
Pure Numpy Implementation of the Coherent Point Drift Algorithm.
This is a pure numpy implementation of the coherent point drift CPD algorithm by Myronenko and Song. It provides three registration methods for point clouds: 1) Scale and rigid registration; 2) Affine registration; and 3) Gaussian regularized non-rigid registration.
The CPD algorithm is a registration method for aligning two point clouds. In this method, the moving point cloud is modelled as a Gaussian Mixture Model (GMM) and the fixed point cloud are treated as observations from the GMM. The optimal transformation parameters maximze the Maximum A Posteriori (MAP) estimation that the observed point cloud is drawn from the GMM.
The registration methods work for 2D and 3D point clouds. For more information, please refer to my blog
$ pip install pycpd
Installation From Source
Clone the repository to a location, referred to as the root folder, in your home directory. For example:
git clone https://github.com/siavashk/pycpd.git $HOME/pycpd
Install the package:
pip install .
For running sample registration examples under /tests, you will need two additional packages.
Scipy (for loading .mat files) and matplotlib (for visualizing the reigstration). These can be downloaded by running:
pip install -r requirements.txt
Each registration method is contained within a single class inside the pycpd subfolder. To try out the registration, you can simply call:
where Transform is either rigid, affine or deformable and Dimension is either 2D or 3D. Note that examples are meant to be run from the root folder.