Event cameras are dynamic vision sensors inspired by the biological retina, characterized by their high dynamic range, high temporal resolution, and low power consumption. These features make them capable of perceiving 3D environments even in extreme conditions. Event data is continuous across the time dimension, which allows a detailed description of each pixel's movements. To fully utilize the temporally dense and continuous nature of event cameras, we propose a novel temporal event stereo, a framework that continuously uses information from previous time steps. This is accomplished through the simultaneous training of an event stereo matching network alongside stereoscopic flow, a new concept that captures all pixel movements from stereo cameras. Since obtaining ground truth for optical flow during training is challenging, we propose a method that uses only disparity maps to train the stereoscopic flow. The performance of event-based stereo matching is enhanced by temporally aggregating information using the flows. We have achieved state-of-the-art performance on the MVSEC and the DSEC datasets. The method is computationally efficient, as it stacks previous information in a cascading manner. The code is available at https://github.com/mickeykang16/TemporalEventStereo.

翻译：事件相机是受生物视网膜启发的动态视觉传感器，具有高动态范围、高时间分辨率和低功耗的特点。这些特性使其能够在极端条件下感知三维环境。事件数据在时间维度上具有连续性，能够详细描述每个像素的运动。为充分利用事件相机时间密集且连续的特性，我们提出了一种新颖的时间事件立体视觉框架，该框架持续利用先前时间步的信息。这是通过同时训练事件立体匹配网络与立体流来实现的——立体流这一新概念能够捕获立体相机中所有像素的运动。由于训练过程中获取光流真值具有挑战性，我们提出了一种仅使用视差图训练立体流的方法。通过利用流进行时间信息聚合，基于事件的立体匹配性能得到显著提升。我们在MVSEC和DSEC数据集上取得了最先进的性能。该方法计算效率高，以前馈级联方式堆叠历史信息。代码发布于https://github.com/mickeykang16/TemporalEventStereo。