Learning to Reconstruct HDR Images from Events, with Applications to Depth and Flow Prediction

Abstract

Event cameras have numerous advantages over traditional cameras, such as low latency, high temporal resolution, and high dynamic range (HDR). We initially investigate the potential of creating intensity images/videos from an adjustable portion of the event data stream via event-based conditional generative adversarial networks (cGANs). Using the proposed framework, we further show the versatility of our method in directly handling similar supervised tasks, such as optical flow and depth prediction. Stacks of space-time coordinates of events are used as the inputs while the proposed framework is trained to predict either the intensity images, optical flows, or depth outputs according to the target task. We further demonstrate the unique capability of our approach in generating HDR images even under extreme illumination conditions, creating non-blurred images under rapid motion, and generating very high frame rate videos up to the temporal resolution of event cameras. The proposed framework is evaluated using a publicly available real-world dataset and a synthetic dataset we prepared by utilizing an event camera simulator.