Depth estimation is crucial in several computer vision applications, and a recent trend aims at inferring such a cue from a single camera through computationally demanding CNNs - precluding their practical deployment in several application contexts characterized by low-power constraints. Purposely, we develop a tiny network tailored to microcontrollers, processing low-resolution images to obtain a coarse depth map of the observed scene. Our solution enables depth perception with minimal power requirements (a few hundreds of mW), accurately enough to pave the way to several high-level applications at-the-edge.

Enabling monocular depth perception at the very edge / Peluso, V.; Cipolletta, A.; Calimera, A.; Poggi, M.; Tosi, F.; Aleotti, F.; Mattoccia, S.. - 2020:(2020), pp. 1581-1583. (Intervento presentato al convegno 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 tenutosi a usa nel 2020) [10.1109/CVPRW50498.2020.00204].

Enabling monocular depth perception at the very edge

Peluso V.;Cipolletta A.;Calimera A.;Aleotti F.;Mattoccia S.
2020

Abstract

Depth estimation is crucial in several computer vision applications, and a recent trend aims at inferring such a cue from a single camera through computationally demanding CNNs - precluding their practical deployment in several application contexts characterized by low-power constraints. Purposely, we develop a tiny network tailored to microcontrollers, processing low-resolution images to obtain a coarse depth map of the observed scene. Our solution enables depth perception with minimal power requirements (a few hundreds of mW), accurately enough to pave the way to several high-level applications at-the-edge.
2020
978-1-7281-9360-1
File in questo prodotto:
File Dimensione Formato  
09150972.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 304.89 kB
Formato Adobe PDF
304.89 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
LPVCV_2020_postprint_iris.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 308.63 kB
Formato Adobe PDF
308.63 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2851214