# An Unsupervised, Iterative N-Dimensional Point-Set Registration Algorithm

## TL;DR

This paper introduces an unsupervised, iterative algorithm for aligning unlabeled N-dimensional point clouds, leveraging linear least squares to consider all point pairings and refine registration without prior correspondence knowledge.

## Contribution

It presents a novel unsupervised iterative registration method that handles unlabeled N-dimensional point clouds using linear least squares and pairwise consideration.

## Key findings

- Effective alignment of unlabeled point clouds in multiple dimensions
- Iterative process converges without prior point correspondence
- Applicable to high-dimensional data sets

## Abstract

An unsupervised, iterative point-set registration algorithm for an unlabeled (i.e. correspondence between points is unknown) N-dimensional Euclidean point-cloud is proposed. It is based on linear least squares, and considers all possible point pairings and iteratively aligns the two sets until the number of point pairs does not exceed the maximum number of allowable one-to-one pairings.