Point-cloud registration using adaptive radial basis functions |
| |
| Authors: | Ju Zhang David Ackland Justin Fernandez |
| |
| Affiliation: | 1. Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand;2. Department of Biomedical Engineering, University of Melbourne, Parkville, Australia;3. Department of Engineering Science, University of Auckland, Auckland, New Zealand |
| |
| Abstract: | Non-rigid registration is a common part of bioengineering model-generation workflows. Compared to common mesh-based methods, radial basis functions can provide more flexible deformation fields due to their meshless nature. We introduce an implementation of RBF non-rigid registration with iterative knot-placement to adaptively reduce registration error. The implementation is validated on surface meshes of the femur, hemi-pelvis, mandible, and lumbar spine. Mean registration surface errors ranged from 0.37 to 0.99?mm, Hausdorff distance from 1.84 to 2.47?mm, and DICE coefficients from 0.97 to 0.99. The implementation is available for use in the free and open-source GIAS2 library. |
| |
| Keywords: | Non-rigid registration registration radial basis function morphing model generation |
|
|
