Inferring Quality in Point Cloud-based 3D Printed Objects using Topological Data Analysis

by   Paul Rosen, et al.
University of South Florida

Assessing the quality of 3D printed models before they are printed remains a challeng- ing problem, particularly when considering point cloud-based models. This paper introduces an approach to quality assessment, which uses techniques from the field of Topological Data Analy- sis (TDA) to compute a topological abstraction of the eventual printed model. Two main tools of TDA, Mapper and persistent homology, are used to analyze both the printed space and empty space created by the model. This abstraction enables investigating certain qualities of the model, with respect to print quality, and identifies potential anomalies that may appear in the final product.


page 2

page 3

page 6


Topological Point Cloud Clustering

We present Topological Point Cloud Clustering (TPCC), a new method to cl...

Point Cloud Quality Assessment using 3D Saliency Maps

Point cloud quality assessment (PCQA) has become an appealing research f...

Quantum Persistent Homology for Time Series

Persistent homology, a powerful mathematical tool for data analysis, sum...

No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models

To improve the viewer's Quality of Experience (QoE) and optimize compute...

Topological Data Analysis with ε-net Induced Lazy Witness Complex

Topological data analysis computes and analyses topological features of ...

Tuning the Performance of a Computational Persistent Homology Package

In recent years, persistent homology has become an attractive method for...

Please sign up or login with your details

Forgot password? Click here to reset