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.

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等特点。该研究提出了一种新颖的时间事件立体视觉框架，通过连续利用先前时间步骤的信息来提高事件立体匹配的性能，并使用视差图来训练立体光流，从而实现对3D环境的感知。在MVSEC和DSEC数据集上实现了最先进的性能。

基于时序事件的立体视觉算法及深度估计