StereoSpike: Depth Learning with a Spiking Neural Network

• URL: http://arxiv.org/abs/2109.13751v1
• Date: Tue, 28 Sep 2021 14:11:36 GMT
• Title: StereoSpike: Depth Learning with a Spiking Neural Network
• Authors: Ulysse Ran\c{c}on, Javier Cuadrado-Anibarro, Benoit R. Cottereau and Timoth\'ee Masquelier
• Abstract summary: We present an end-to-end neuromorphic approach to depth estimation. We use a Spiking Neural Network (SNN) with a slightly modified U-Net-like encoder-decoder architecture, that we named StereoSpike. We demonstrate that this architecture generalizes very well, even better than its non-spiking counterparts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Depth estimation is an important computer vision task, useful in particular for navigation in autonomous vehicles, or for object manipulation in robotics. Here we solved it using an end-to-end neuromorphic approach, combining two event-based cameras and a Spiking Neural Network (SNN) with a slightly modified U-Net-like encoder-decoder architecture, that we named StereoSpike. More specifically, we used the Multi Vehicle Stereo Event Camera Dataset (MVSEC). It provides a depth ground-truth, which was used to train StereoSpike in a supervised manner, using surrogate gradient descent. We propose a novel readout paradigm to obtain a dense analog prediction -- the depth of each pixel -- from the spikes of the decoder. We demonstrate that this architecture generalizes very well, even better than its non-spiking counterparts, leading to state-of-the-art test accuracy. To the best of our knowledge, it is the first time that such a large-scale regression problem is solved by a fully spiking network. Finally, we show that low firing rates (<10%) can be obtained via regularization, with a minimal cost in accuracy. This means that StereoSpike could be efficiently implemented on neuromorphic chips, opening the door for low power and real time embedded systems.

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