Event-based cameras can overpass frame-based cameras limitations for
important tasks such as high-speed motion detection during self-driving cars
navigation in low illumination conditions. The event cameras' high temporal
resolution and high dynamic range, allow them to work in fast motion and
extreme light scenarios. However, conventional computer vision methods, such as
Deep Neural Networks, are not well adapted to work with event data as they are
asynchronous and discrete. Moreover, the traditional 2D-encoding representation
methods for event data, sacrifice the time resolution. In this paper, we first
improve the 2D-encoding representation by expanding it into three dimensions to
better preserve the temporal distribution of the events. We then propose
3D-FlowNet, a novel network architecture that can process the 3D input
representation and output optical flow estimations according to the new
encoding methods. A self-supervised training strategy is adopted to compensate
the lack of labeled datasets for the event-based camera. Finally, the proposed
network is trained and evaluated with the Multi-Vehicle Stereo Event Camera
(MVSEC) dataset. The results show that our 3D-FlowNet outperforms
state-of-the-art approaches with less training epoch (30 compared to 100 of
Spike-FlowNet).

本文提出了一种改进的 2D 编码事件数据的 3D 表达方法，并介绍了 3D-FlowNet 网络体系结构以进行光流估计，并采用自监督训练策略来弥补事件相机标记数据的缺乏。结果表明，我们的 3D-FlowNet 具有更好的性能。