Point Cloud Data Simulation and Modelling with Aize Workspace

TL;DR

This paper explores the use of simulated point cloud data for surface reconstruction and semantic segmentation to enhance digital twin data modeling, providing foundational insights for future data contextualization efforts.

Contribution

It introduces preliminary results on surface reconstruction and semantic segmentation models trained with simulated data for digital twins.

Findings

Initial results demonstrate feasibility of using simulated data for surface reconstruction.

Semantic segmentation models trained on simulated data show promising accuracy.

Lays groundwork for future digital twin data contextualization.

Abstract

This work takes a look at data models often used in digital twins and presents preliminary results specifically from surface reconstruction and semantic segmentation models trained using simulated data. This work is expected to serve as a ground work for future endeavours in data contextualisation inside a digital twin.