MobileMDE
10+
Okudawunilodiwe
Wonke umuntu
info
Mayelana nalolu hlelo lokusebenza
Uhlelo lokusebenza luhlola imodeli ye-TFLite noma i-DNN-based Monocular Depth Estimation (MDE) esekelwe ephepheni:
L. Papa, E. Alati, P. Russo kanye no-I. Amerini, "SPEED: Separable Pyramidal Pooling Er-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings," in IEEE Access, vol. 10, amakhasi 44881-44890, 2022
https://doi.org/10.1109/ACCESS.2022.3170425
https://alcorlab.diag.uniroma1.it
L. Papa, E. Alati, P. Russo kanye no-I. Amerini, "SPEED: Separable Pyramidal Pooling Er-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings," in IEEE Access, vol. 10, amakhasi 44881-44890, 2022
https://doi.org/10.1109/ACCESS.2022.3170425
https://alcorlab.diag.uniroma1.it
Kubuyekezwe ngo-
Ukuphepha kuqala ngokuqonda ukuthi onjiniyela baqoqa futhi babelane kanjani ngedatha yakho. Ubumfihlo bedatha nezinqubo zokuphepha zingahluka kuye ngokusebenzisa kwakho, isifunda, nobudala. Unjiniyela unikeze lolu lwazi futhi angalubuyekeza ngokuhamba kwesikhathi.
Ayikho idatha eyabiwe nezinkampani zangaphandle
Funda kabanzi mayelana nendlela onjiniyela abaveza ngayo ukwabelana
Ayikho idatha eqoqiwe
Funda kabanzi mayelana nokuthi onjiniyela bakuveza kanjani ukuqoqwa
Yini entsha
- switched depth and RGB
- increased info font size
- increased info font size
Ukusekelwa kwe-app
Mayelana nonjiniyela
Gabriele Proietti Mattia
gabrielepmattia.apps@gmail.com
Via Ariosto, 25
00185 Roma
Italy
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