论文标题
在对比度最大化框架中，快速的几何正规化器可减轻事件崩溃
A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework
论文作者
论文摘要
事件摄像机是新兴的视觉传感器，其优势适用于各种应用，例如自动机器人。对比度最大化（CMAX）使用事件提供了最新的运动估计准确性，可能会遭受称为事件崩溃的过度拟合问题。先前的工作在计算上很昂贵，或者无法减轻过度拟合，这破坏了CMAX框架的好处。我们根据几何原理提出了一种新颖的计算有效正常器，以减轻事件崩溃。实验表明，提出的正规器可实现最新的准确性结果，而其计算复杂性的降低使其比以前的方法快两到四倍。据我们所知，我们的常规化器是事件崩溃的唯一有效解决方案，而无需交易运行时。我们希望我们的工作为未来的应用打开了释放活动相机优势的应用。
Event cameras are emerging vision sensors and their advantages are suitable for various applications such as autonomous robots. Contrast maximization (CMax), which provides state-of-the-art accuracy on motion estimation using events, may suffer from an overfitting problem called event collapse. Prior works are computationally expensive or cannot alleviate the overfitting, which undermines the benefits of the CMax framework. We propose a novel, computationally efficient regularizer based on geometric principles to mitigate event collapse. The experiments show that the proposed regularizer achieves state-of-the-art accuracy results, while its reduced computational complexity makes it two to four times faster than previous approaches. To the best of our knowledge, our regularizer is the only effective solution for event collapse without trading off runtime. We hope our work opens the door for future applications that unlocks the advantages of event cameras.
