• Corpus ID: 238198645

StereoSpike: Depth Learning with a Spiking Neural Network

@article{Ranon2021StereoSpikeDL,
  title={StereoSpike: Depth Learning with a Spiking Neural Network},
  author={Ulysse Rançon and Javier Cuadrado-Anibarro and Benoit R. Cottereau and Timoth{\'e}e Masquelier},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.13751}
}
Depth estimation is an important computer vision task, useful in particular for navigation in autonomous vehicles, or for object manipulation in robotics. Here, we propose to solve it using StereoSpike , an end-to-end neuromorphic approach, combining two event-based cameras and a Spiking Neural Network (SNN) with a modified U-Net-like encoder-decoder architecture. More specifically, we used the Multi Vehicle Stereo Event Camera Dataset (MVSEC). It provides a depth ground-truth, which was used to… 

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