Enabling Monocular Depth Perception at the Very Edge

Valentino Peluso, Antonio Cipolletta, Andrea Calimera, Matteo Poggi, Fabio Tosi, Filippo Aleotti, Stefano Mattoccia

CVPRW 2020 pp. 1581-1583

doi:10.1109/CVPRW50498.2020.00204 /cvprw/2020/peluso2020cvprw-enabling/

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.

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Cite

Text

Peluso et al. "Enabling Monocular Depth Perception at the Very Edge." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00204

Markdown

[Peluso et al. "Enabling Monocular Depth Perception at the Very Edge." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/peluso2020cvprw-enabling/) doi:10.1109/CVPRW50498.2020.00204

BibTeX

@inproceedings{peluso2020cvprw-enabling,
  title     = {{Enabling Monocular Depth Perception at the Very Edge}},
  author    = {Peluso, Valentino and Cipolletta, Antonio and Calimera, Andrea and Poggi, Matteo and Tosi, Fabio and Aleotti, Filippo and Mattoccia, Stefano},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2020},
  pages     = {1581-1583},
  doi       = {10.1109/CVPRW50498.2020.00204},
  url       = {https://mlanthology.org/cvprw/2020/peluso2020cvprw-enabling/}
}