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On-Device 3D Foot Reconstruction for Digital Sizing.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
On-Device 3D Foot Reconstruction for Digital Sizing./
作者:
Hassan, Najah.
面頁冊數:
1 online resource (89 pages)
附註:
Source: Masters Abstracts International, Volume: 84-05.
Contained By:
Masters Abstracts International84-05.
標題:
Computer science. -
電子資源:
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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Source: Masters Abstracts International, Volume: 84-05.
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Includes bibliographical references
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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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click for full text (PQDT)
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