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Event cameras do not produce images, but rather a continuous flow of events,which encode changes of illumination for each pixel independently andasynchronously. While they output temporally rich information, they lack anydepth information which could facilitate their use with other sensors. LiDARscan provide this depth information, but are by nature very sparse, which makesthe depth-to-event association more complex. Furthermore, as events representchanges of illumination, they might also represent changes of depth;associating them with a single depth is therefore inadequate. In this work, wepropose to address these issues by fusing information from an event camera anda LiDAR using a learning-based approach to estimate accurate dense depth maps.To solve the "potential change of depth" problem, we propose here to estimatetwo depth maps at each step: one "before" the events happen, and one "after"the events happen. We further propose to use this pair of depths to compute adepth difference for each event, to give them more context. We train andevaluate our network, ALED, on both synthetic and real driving sequences, andshow that it is able to predict dense depths with an error reduction of up to61% compared to the current state of the art. We also demonstrate the qualityof our 2-depths-to-event association, and the usefulness of the depthdifference information. Finally, we release SLED, a novel synthetic datasetcomprising events, LiDAR point clouds, RGB images, and dense depth maps.

Learning to Estimate Two Dense Depths from LiDAR and Event Data