Algorithmic approaches for performing patient alignment when using the HoloLens as a surgical navigation device.
Part of the 2021-2022 research project at TU Delft.
The repository contains different algorithms for performing rought point cloud alignment:
- Fast point feature histograms
- Principal component analysis
- Manual point selection
After the rough point cloud registration is performed using one of these algorithms, the registration is refined using the Iterative Closest Point algorithm.
Each algorithm is contained in its own package. Every package contains a method which performs rough and precise point cloud registration on the provided point clouds. This method provides an easy to use endpoint for point cloud registration.
- The
pca_icp_alignment
method performs PCA rough registration followed by ICP precise registration - The
fpfh_icp_alignment
method performs FPFH rough registration followed by ICP precise registration - The
mps_icp_alignment
method performs manual point selection rough registration followed by ICP registration
To use a given algorithm, just import the algorithm from the package. For example:
from pca.pca_icp_alignment import pca_icp_alignment
Every algorithm needs at least the following input parameters:
source
: The source point cloud (the point cloud that should be transformed)target_depth_sensor
: The depth-sensor version of the target point cloud (the point cloud that the source should be transformed to)target_pointer
: The pointer version of the target point cloud (the point cloud that the source should be transformed to)
Any additional supported parameters are present in the documentation.