國立虎尾科技大學 |

On-Device 3D Foot Reconstruction for Digital Sizing.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	On-Device 3D Foot Reconstruction for Digital Sizing./
Author:	Hassan, Najah.
Description:	1 online resource (89 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-05.
Contained By:	Masters Abstracts International84-05.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798357547576

On-Device 3D Foot Reconstruction for Digital Sizing.
Hassan, Najah.

On-Device 3D Foot Reconstruction for Digital Sizing. - 1 online resource (89 pages)

Source: Masters Abstracts International, Volume: 84-05.

Thesis (M.A.S.)--University of Toronto (Canada), 2022.

Includes bibliographical references

3D surface reconstruction for the extraction of anthropometric data is a proven methodology that often relies on depth sensing cameras and has produced results of high accuracy. We develop a similar reconstruction pipeline that allows the extraction of foot measurements using a portable smartphone device. Our solution uses a single monocular camera and relies on augmented reality depth sensing available on smartphone devices to scan and reconstruct a foot. We use a machine learning model for filtering our data that is lightweight enough to be run on a mobile device and has an accuracy of 81.4%. The final proposed solution runs on a Google Pixel 3 device. It has a scanning time of 10 seconds and a reconstruction time of 15 seconds. The methods employed have shown that it is possible to get foot length and width dimensions with an error as small as 0.2 cm.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798357547576Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

3D scanningIndex Terms--Genre/Form:

554714
Electronic books.

On-Device 3D Foot Reconstruction for Digital Sizing.
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520 $a 3D surface reconstruction for the extraction of anthropometric data is a proven methodology that often relies on depth sensing cameras and has produced results of high accuracy. We develop a similar reconstruction pipeline that allows the extraction of foot measurements using a portable smartphone device. Our solution uses a single monocular camera and relies on augmented reality depth sensing available on smartphone devices to scan and reconstruct a foot. We use a machine learning model for filtering our data that is lightweight enough to be run on a mobile device and has an accuracy of 81.4%. The final proposed solution runs on a Google Pixel 3 device. It has a scanning time of 10 seconds and a reconstruction time of 15 seconds. The methods employed have shown that it is possible to get foot length and width dimensions with an error as small as 0.2 cm.
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653 $a 3D surface reconstruction
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