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.File | Dimensione | Formato | |
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