论文标题
混合事件相机的异步Kalman滤波器
An Asynchronous Kalman Filter for Hybrid Event Cameras
论文作者
论文摘要
事件摄像机非常适合捕获HDR的视觉信息而不会模糊，但在静态或缓慢变化的场景上表现不佳。相反，传统的图像传感器有效地测量了缓慢变化的场景的绝对强度，但在高动态范围或快速变化的场景上表现不佳。在本文中，我们介绍了一个基于事件的视频重建管道，用于高动态范围（HDR）方案。所提出的算法包括一个框架增强预处理步骤，该步骤使用事件进行介绍并暂时插值框架数据。然后，在两个传感器的统一不确定性模型下，使用新型异步Kalman滤波器融合增强帧和事件数据。我们的实验结果在具有挑战性的照明条件和快速动作的公开可用数据集上进行了评估，以及带有HDR参考的新数据集。所提出的算法在绝对强度误差（降低48％）和图像相似性指数（平均11％提高）中的最先进方法均优于最先进的方法。
Event cameras are ideally suited to capture HDR visual information without blur but perform poorly on static or slowly changing scenes. Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on high dynamic range or quickly changing scenes. In this paper, we present an event-based video reconstruction pipeline for High Dynamic Range (HDR) scenarios. The proposed algorithm includes a frame augmentation pre-processing step that deblurs and temporally interpolates frame data using events. The augmented frame and event data are then fused using a novel asynchronous Kalman filter under a unifying uncertainty model for both sensors. Our experimental results are evaluated on both publicly available datasets with challenging lighting conditions and fast motions and our new dataset with HDR reference. The proposed algorithm outperforms state-of-the-art methods in both absolute intensity error (48% reduction) and image similarity indexes (average 11% improvement).
