Reconstruction of a 3D Human Foot Shape Model Based on a Video Stream Using Photogrammetry and Deep Neural Networks

Lev Shilov, Semen Shanshin, Aleksandr Romanov, Anastasia Fedotova, Anna Kurtukova, Evgeny Kostyuchenko and Ivan Sidorov
Additional contact information
Lev Shilov: Department of Security, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia
Semen Shanshin: Department of Security, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia
Aleksandr Romanov: Department of Security, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia
Anastasia Fedotova: Department of Security, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia
Anna Kurtukova: Department of Security, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia
Evgeny Kostyuchenko: Department of Security, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia
Ivan Sidorov: Irkutsk Supercomputer Center of SB RAS, 134 Lermontova, 664033 Irkutsk, Russia

Future Internet, 2021, vol. 13, issue 12, 1-21

Abstract: Reconstructed 3D foot models can be used for 3D printing and further manufacturing of individual orthopedic shoes, as well as in medical research and for online shoe shopping. This study presents a technique based on the approach and algorithms of photogrammetry. The presented technique was used to reconstruct a 3D model of the foot shape, including the lower arch, using smartphone images. The technique is based on modern computer vision and artificial intelligence algorithms designed for image processing, obtaining sparse and dense point clouds, depth maps, and a final 3D model. For the segmentation of foot images, the Mask R-CNN neural network was used, which was trained on foot data from a set of 40 people. The obtained accuracy was 97.88%. The result of the study was a high-quality reconstructed 3D model. The standard deviation of linear indicators in length and width was 0.95 mm, with an average creation time of 1 min 35 s recorded. Integration of this technique into the business models of orthopedic enterprises, Internet stores, and medical organizations will allow basic manufacturing and shoe-fitting services to be carried out and will help medical research to be performed via the Internet.

Keywords: 3D foot reconstruction; photogrammetry; segmentation; orthopedic shoes; deep neural networks (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/13/12/315/pdf (application/pdf)
https://www.mdpi.com/1999-5903/13/12/315/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:13:y:2021:i:12:p:315-:d:702372

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().