Event cameras are bio-inspired sensors providing significant advantages over standard cameras such as low latency, high temporal resolution, and high dynamic range. We propose a novel structured-light system using an event camera to tackle the problem of accurate and high-speed depth sensing. Our setup consists of an event camera and a laser-point projector that uniformly illuminates the scene in a raster scanning pattern during 16 ms. Previous methods match events independently of each other, and so they deliver noisy depth estimates at high scanning speeds in the presence of signal latency and jitter. In contrast, we optimize an energy function designed to exploit event correlations, called spatio-temporal consistency. The resulting method is robust to event jitter and therefore performs better at higher scanning speeds. Experiments demonstrate that our method can deal with high-speed motion and outperform state-of-the-art 3D reconstruction methods based on event cameras, reducing the RMSE by 83% on average, for the same acquisition time.
翻译:事件摄像机是生物激励型传感器,比低延迟度、高时间分辨率和高动态范围等标准相机具有很大的优势。 我们提议采用新型结构灯光系统,使用事件相机来解决准确和高速深度感测问题。 我们的设置由事件相机和激光点投影仪组成,在16米期间以光栅扫描模式统一亮出场景。 以往的方法彼此独立地匹配事件,因此它们以高扫描速度以高扫描速度在信号延迟度和急速下提供噪音深度估计值。 相反,我们优化了一种能源功能,目的是利用事件相关性,称为时空一致性。 由此产生的方法对事件迅速性非常有力,因此在更高的扫描速度下运行得更好。 实验表明,我们的方法可以使用事件摄像机来处理高速运动和超速的3D重建方法,在同一时间以平均时间将RME减少83%。