Event cameras are a kind of bio-inspired sensors that generate data when the brightness changes, which are of low-latency and high dynamic range (HDR). However, due to the nature of the sparse event stream, event-based mapping can only obtain sparse or semi-dense edge 3D maps. By contrast, standard cameras provide complete frames. To leverage the complementarity of event-based and standard frame-based cameras, we propose a fusion strategy for dense mapping in this paper. We first generate an edge map from events, and then fill the map using frames to obtain the dense depth map. We propose "filling score" to evaluate the quality of filled results and show that our strategy can increase the number of existing semi-dense 3D map.
翻译:事件相机是一种生物感应器,在亮度变化时产生数据,亮度变化是低延度和高动态范围(HDR)的。然而,由于稀有事件流的性质,以事件为基础的绘图只能获得稀有或半浓度边缘3D地图。相反,标准相机提供完整的框架。为了利用事件和标准框架相机的互补性,我们提出了本文中密集绘图的聚变战略。我们首先从事件中绘制一幅边缘地图,然后用框架填满地图,以获取密度深度地图。我们建议“填充分”以评价填充结果的质量,并表明我们的战略可以增加现有半浓度3D地图的数量。