  Depth estimation is essential for various important real-world applications
such as autonomous driving. However, it suffers from severe performance
degradation in high-velocity scenario since traditional cameras can only
capture blurred images. To deal with this problem, the spike camera is designed
to capture the pixel-wise luminance intensity at high frame rate. However,
depth estimation with spike camera remains very challenging using traditional
monocular or stereo depth estimation algorithms, which are based on the
photometric consistency. In this paper, we propose a novel Uncertainty-Guided
Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo
depth estimation networks for spike camera. Our framework is motivated by the
fact that stereo spike depth estimation achieves better results at close range
while monocular spike depth estimation obtains better results at long range.
Therefore, we introduce a dual-task depth estimation architecture with a joint
training strategy and estimate the distributed uncertainty to fuse the
monocular and stereo results. In order to demonstrate the advantage of spike
depth estimation over traditional camera depth estimation, we contribute a
spike-depth dataset named CitySpike20K, which contains 20K paired samples, for
spike depth estimation. UGDF achieves state-of-the-art results on CitySpike20K,
surpassing all monocular or stereo spike depth estimation baselines. We conduct
extensive experiments to evaluate the effectiveness and generalization of our
method on CitySpike20K. To the best of our knowledge, our framework is the
first dual-task fusion framework for spike camera depth estimation. Code and
dataset will be released.
