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Доброго времени суток, уважаемые форумчане) Пишу научную статью) Нужна Ваша помощь, т.к. я в данной области ничего не понимаю ((..
Допустим:
есть объемный след
такой
http://data:image/jpeg;base64,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 или такой
его нужно отсканировать и получить 3d модель ступни ( подошвы)
Что-то вроде такого
1)Возможно ли отсканировать объемный след?
2)На сколько быстро и качественно это можно сделать?
а)В лабораторных условиях (стационарным сканером)
б)Вне лаборатории (переносным сканером)
3)Какое имеет значение грунт для сканирования? ( песок, земля, глина, скала и тд)
4)Какое оборудование потребуется дополнительно?
Буду рад любому материалу по данной теме( фото, видео, статьи, модели сканеров, программное обеспечение, советы и тд)
Цена оборудования роли не играет, главное- качество сканирования.
Заранее спасибо))
Допустим:
есть объемный след
такой
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 или такой
его нужно отсканировать и получить 3d модель ступни ( подошвы)
Что-то вроде такого
1)Возможно ли отсканировать объемный след?
2)На сколько быстро и качественно это можно сделать?
а)В лабораторных условиях (стационарным сканером)
б)Вне лаборатории (переносным сканером)
3)Какое имеет значение грунт для сканирования? ( песок, земля, глина, скала и тд)
4)Какое оборудование потребуется дополнительно?
Буду рад любому материалу по данной теме( фото, видео, статьи, модели сканеров, программное обеспечение, советы и тд)
Цена оборудования роли не играет, главное- качество сканирования.
Заранее спасибо))
Последнее редактирование: