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.

使用神经形态学方法和多车立体事件相机数据集 (MVSEC)，结合双脉冲事件相机和脉冲神经网络 (SNN) 的编码器 - 解码器架构，开发了一种新颖的神经元模仿方法 (StereoSpike) 来解决深度估计问题，并得到了最先进的测试准确性。