# Point cloud registration: matching a maximal common subset on pointclouds with noise (with 2D implementation)

## TL;DR

This paper addresses the challenge of matching maximal common subsets of 2D point clouds with noise and outliers, proposing an energy-based algorithm inspired by electrostatics to identify the largest matching subclouds.

## Contribution

It introduces a novel algorithm that optimizes a potential energy function to find maximum common subsets in noisy 2D point clouds, applicable to fingerprint matching.

## Key findings

- Successfully identifies large matching subclouds despite noise and outliers
- Demonstrates effectiveness of electrostatics-inspired energy optimization
- Applicable to real-world fingerprint matching scenarios

## Abstract

We analyze the problem of determining whether 2 given point clouds in 2D, with any distinct cardinality and any number of outliers, have subsets of the same size that can be matched via a rigid motion. This problem is important, for example, in the application of fingerprint matching with incomplete data. We propose an algorithm that, under assumptions on the noise tolerance, allows to find corresponding subclouds of the maximum possible size. Our procedure optimizes a potential energy function to do so, which was first inspired in the potential energy interaction that occurs between point charges in electrostatics.