# Filling missing data in point clouds by merging structured and unstructured point clouds

## TL;DR

This paper introduces a method to improve structured point clouds from CT scans by merging them with unstructured point clouds from laser scans, creating a 'half-structured' dataset that better fills in missing data.

## Contribution

The paper proposes a novel approach to extend structured point clouds with unstructured data, enhancing data completeness where traditional surface reconstruction fails.

## Key findings

- Improved point cloud completeness through merging techniques.
- Enhanced data quality with 'half-structured' point clouds.
- Potential for better surface reconstruction results.

## Abstract

Point clouds arising from structured data, mainly as a result of CT scans, provides special properties on the distribution of points and the distances between those. Yet often, the amount of data provided can not compare to unstructured point clouds, i.e. data that arises from 3D light scans or laser scans. This article hereby proposes an approach to extend structured data and enhance the quality by inserting selected points from an unstructured point cloud. The resulting point cloud still has a partial structure that is called "half-structure". In this way, missing data that can not be optimally recovered through other surface reconstruction methods can be completed.