Event-based cameras are becoming increasingly popular for their ability to
capture high-speed motion with low latency and high dynamic range. However,
generating videos from events remains challenging due to the highly sparse and
varying nature of event data. To address this, in this study, we propose
HyperE2VID, a dynamic neural network architecture for event-based video
reconstruction. Our approach uses hypernetworks and dynamic convolutions to
generate per-pixel adaptive filters guided by a context fusion module that
combines information from event voxel grids and previously reconstructed
intensity images. We also employ a curriculum learning strategy to train the
network more robustly. Experimental results demonstrate that HyperE2VID
achieves better reconstruction quality with fewer parameters and faster
inference time than the state-of-the-art methods.

本研究提出了一种基于动态神经网络架构的事件重建算法 HyperE2VID，使用超网络和动态卷积生成自适应滤波器，并结合上下文融合模块，以从事件体素网格和先前重建的强度图像中获得信息，采用课程式学习策略来训练网络，实验结果表明，HyperE2VID 具有更好的重建质量，参数更少，推理时间更短。