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.

Peluso V., Cipolletta A., Calimera A., Poggi M., Tosi F., Aleotti F., et al. (2020). Enabling monocular depth perception at the very edge. IEEE Computer Society [10.1109/CVPRW50498.2020.00204].

Enabling monocular depth perception at the very edge

Poggi M.;Tosi F.;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
2020 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
1581
1583
Peluso V., Cipolletta A., Calimera A., Poggi M., Tosi F., Aleotti F., et al. (2020). Enabling monocular depth perception at the very edge. IEEE Computer Society [10.1109/CVPRW50498.2020.00204].
Peluso V.; Cipolletta A.; Calimera A.; Poggi M.; Tosi F.; Aleotti F.; Mattoccia S.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/771841
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