3DBODY.TECH 2019 - Paper 19.015

P. Laurin, et al., "Shapeshift 3D Repair - A Fully Automated and Unsupervised Cloud API for the Reliable Reconstruction of Raw 3D Surface Scans Data", in Proc. of 3DBODY.TECH 2019 - 10th Int. Conf. and Exh. on 3D Body Scanning and Processing Technologies, Lugano, Switzerland, 22-23 Oct. 2019, pp. 15-26, doi:10.15221/19.015.


Shapeshift 3D Repair - A Fully Automated and Unsupervised Cloud API for the Reliable Reconstruction of Raw 3D Surface Scans Data


Patrick LAURIN, Daniel BELAND, Jonathan BORDUAS

Technologies ShapeShift 3D Inc., Montreal QC, Canada


This paper presents a study to quantify the reliability of the automatic reconstruction tool Shapeshift 3D Repair to create watertight, genus 0, precise and accurate 3D surface scan of the human body. Our methodology uses a precise baseline 3D scan acquired from a full body 3D scanner as an input of a scanning process simulator that emulates the properties of a common 3D scanner, the Structure by Occipital, and behavior of a typical untrained handheld 3D scanner operator. The output of the simulator is a raw scan (noisy and incomplete). Afterward, the raw scan is fed to Shapeshift 3D Repair which outputs a reconstructed scan. We express the reliability of the process in terms of Standard Error of Measurement (SEM). Using the girth difference between the baseline scan and the reconstructed scan, we express the compatibility in terms of Signed Mean Difference (Bias) and Mean Absolute Error (MAE). We compare our results with common reconstruction methods found in the literature and with other studies about the reliability of 3D Scanning, Plaster Casting and Traditional Anthropometry.
Context: To create custom medical devices and wearable, the patient's 3D geometry can be acquired using a 3D scanner. The raw 3D scans require post-processing as they are often noisy and incomplete. While organizations using 3D scanners put in place training programs, scans are often of poor quality. To this day, this issue is a hindrance to business models centered on 3D scanning such as the novel 3Dscan-to-3Dprint business model; inadequate scans must be manually corrected by the operators, which is a time-consuming offline process. The study is focused on the simulation of the scanning process and the scan reconstruction of the knee.
Results: A fully automated and unsupervised cloud processing services for the reconstruction of the knee has been implemented and is ready to be tested by user and vendors of 3D scanners. Reconstructed scans exhibit leg, knee and max thigh girth error under 0.1 cm, 0.3 and 0.4 cm respectively with 95% confidence level while producing properly defined surface that are manifold, genus 0, have good triangle aspect ratio, and have a single surface. With the recent boom of devices featuring an embedded 3D scanner, we believe that in given time, our technology can be accessible to millions of users without the needs of industry-specific hardware or skills.


Full paper: 19015laurin.pdf
Proceedings: 3DBODY.TECH 2019, 22-23 Oct. 2019, Lugano, Switzerland
Pages: 15-26
DOI: 10.15221/19.015

Copyright notice

© Hometrica Consulting - Dr. Nicola D'Apuzzo, Switzerland, www.hometrica.ch.
Reproduction of the proceedings or any parts thereof (excluding short quotations for the use in the preparation of reviews and technical and scientific papers) may be made only after obtaining the specific approval of the publisher. The papers appearing in the proceedings reflect the author's opinions. Their inclusion in these publications does not necessary constitute endorsement by the editor or by the publisher. Authors retain all rights to individual papers.

Proceedings of 3DBODY.TECH International Conferences on 3D Body Scanning & Processing Technologies, © Hometrica Consulting, Switzerland