Correspondence Free Registration Through a Point-to-Model Distance Minimization
Abstract
This paper presents a novel formulation, which derives in a smooth minimization problem, to tackle the rigid registration between a given point set and a model set. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, we propose to describe the model set by means of an implicit representation. It allows a new definition of the registration error, which works beyond the point level representation. Moreover, it could be used in a gradient-based optimization framework. The proposed approach consists of two stages. Firstly, a novel formulation is proposed that relates the registration parameters with the distance between the model and data set. Secondly, the registration parameters are obtained by means of the Levengberg-Marquardt algorithm. Experimental results and comparisons with state of the art show the validity of the proposed framework.
Cite
Text
Rouhani and Sappa. "Correspondence Free Registration Through a Point-to-Model Distance Minimization." IEEE/CVF International Conference on Computer Vision, 2011. doi:10.1109/ICCV.2011.6126491Markdown
[Rouhani and Sappa. "Correspondence Free Registration Through a Point-to-Model Distance Minimization." IEEE/CVF International Conference on Computer Vision, 2011.](https://mlanthology.org/iccv/2011/rouhani2011iccv-correspondence/) doi:10.1109/ICCV.2011.6126491BibTeX
@inproceedings{rouhani2011iccv-correspondence,
title = {{Correspondence Free Registration Through a Point-to-Model Distance Minimization}},
author = {Rouhani, Mohammad and Sappa, Angel Domingo},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2011},
pages = {2150-2157},
doi = {10.1109/ICCV.2011.6126491},
url = {https://mlanthology.org/iccv/2011/rouhani2011iccv-correspondence/}
}