Abstract
Event cameras are novel vision sensors that output pixel-level brightness changes ("events") instead of traditional video frames. These asynchronous sensors offer several advantages over traditional cameras, such as, high temporal resolution, very high dynamic range, and no motion blur. To unlock the potential of such sensors, motion compensation methods have been recently proposed. We present a collection and taxonomy of twenty two objective functions to analyze event alignment in motion compensation approaches (Fig. 1). We call them Focus Loss Functions since they have strong connections with functions used in traditional shape-from-focus applications. The proposed loss functions allow bringing mature computer vision tools to the realm of event cameras. We compare the accuracy and runtime performance of all loss functions on a publicly available dataset, and conclude that the variance, the gradient and the Laplacian magnitudes are among the best loss functions. The applicability of the loss functions is shown on multiple tasks: rotational motion, depth and optical flow estimation. The proposed focus loss functions allow to unlock the outstanding properties of event cameras.
Abstract (translated)
事件照相机是一种新型视觉传感器，它输出像素级亮度变化（“事件”）而不是传统的视频帧。与传统相机相比，这些异步传感器具有一些优势，例如，时间分辨率高、动态范围高和无运动模糊。为了释放这种传感器的潜能，最近提出了运动补偿方法。我们提供了22个目标函数的集合和分类，以分析运动补偿方法中的事件对齐（图1）。我们称它们为焦点丢失函数，因为它们与焦点应用程序中传统形状中使用的函数有很强的联系。提出的损失函数允许将成熟的计算机视觉工具带到事件摄像机领域。我们比较了公开数据集上所有损失函数的准确性和运行时性能，得出方差、梯度和拉普拉斯幅度是最佳损失函数。损失函数的适用性体现在多种任务上：旋转运动、深度和光流估计。提出的焦距损失函数允许解锁事件摄像头的突出属性。
URL
https://arxiv.org/abs/1904.07235
