nricp - Non-rigid iterative closest point

https://github.com/charlienash/nricp

![face demo] (https://github.com/charlienash/nricp/blob/master/demos/faceDemo.jpg)

nricp is a MATLAB implementation of a non-rigid variant of the iterative closest point algorithm. It can be used to register 3D surfaces or point-clouds. The method is described in the following paper:

'Optimal Step Nonrigid ICP Algorithms for Surface Registration', Amberg, Romandhani and Vetter, CVPR, 2007.

Features:

• Non-rigid and local deformations of a template surface or point cloud.
• Iterative stiffness reduction allows for global intitial transformations that become increasingly localised.
• Optional initial rigid registration using standard iterative closest point.
• Optional bi-directional distance metric which encourages surface deformations to cover more of the target surface
• Handles missing data in the target surface by ignoring correspondences with points on target edges.

![missing data demo] (https://github.com/charlienash/nricp/blob/master/demos/faceDemoMissing.jpg)

Dependencies

Requires:

• geom3d - http://uk.mathworks.com/matlabcentral/fileexchange/24484-geom3d
• Toolbox Graph - http://uk.mathworks.com/matlabcentral/fileexchange/5355-toolbox-graph
• Iterative Closest Point - http://uk.mathworks.com/matlabcentral/fileexchange/27804-iterative-closest-point

Installation

Download the nricp directory and the dependencies and add them to your MATLAB path.

Attribution

If you use this implementation in your academic projects, please cite the paper by Amberg et al:

@inproceedings{amberg2007optimal,
  title={Optimal step nonrigid icp algorithms for surface registration},
  author={Amberg, Brian and Romdhani, Sami and Vetter, Thomas},
  booktitle={Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on},
  pages={1--8},
  year={2007},
  organization={IEEE}
}

Contact

charlie.tc.nash@gmail.com