Filling missing data in point clouds by merging structured and unstructured point clouds
This addresses data completion issues in 3D reconstruction for applications like medical imaging or scanning, but it appears incremental as it builds on existing point cloud fusion techniques.
The paper tackles the problem of missing data in structured point clouds (e.g., from CT scans) by merging them with unstructured point clouds (e.g., from 3D scans) to create a 'half-structured' result, enhancing quality and completing gaps that other methods cannot optimally recover.
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.