Resonant Deformable Matching: Simultaneous Registration and Reconstruction
Abstract
In the past decade we have seen the emergence of many efficient algorithms for estimating non-rigid deformations registering a template to target features. Registration of density functions is particularly popular. In contrast to the success enjoyed by the density function representation, we have not seen similar success with the signed distance function representation. Resonant deformable matching (RDM) simultaneously estimates a non-rigid deformation and a set of unknown target normal directions by registering fields comprising signed distance and probability density information. Resonance occurs as the reconstruction estimate comes into agreement with the registered template. We perform experiments probing two problems: point-set registration and normal estimation. RDM compares favorably to top tier point registration and graph algorithms in terms of registration and reconstruction metrics.
Cite
Text
Corring and Rangarajan. "Resonant Deformable Matching: Simultaneous Registration and Reconstruction." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-46466-4_4Markdown
[Corring and Rangarajan. "Resonant Deformable Matching: Simultaneous Registration and Reconstruction." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/corring2016eccv-resonant/) doi:10.1007/978-3-319-46466-4_4BibTeX
@inproceedings{corring2016eccv-resonant,
title = {{Resonant Deformable Matching: Simultaneous Registration and Reconstruction}},
author = {Corring, John and Rangarajan, Anand},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {51-68},
doi = {10.1007/978-3-319-46466-4_4},
url = {https://mlanthology.org/eccv/2016/corring2016eccv-resonant/}
}