论文标题
事件崩溃以对比最大化框架
Event Collapse in Contrast Maximization Frameworks
论文作者
论文摘要
对比度最大化（CMAX）是一个框架，可在几个基于事件的计算机视觉任务（例如EGO-MOTION或光流估计）上提供最先进的结果。但是，它可能会遇到一个称为事件崩溃的问题，这是一种不希望的解决方案，其中事件被扭曲成太少的像素。由于先前的工作在很大程度上忽略了这个问题或提出的解决方法，因此必须详细分析这种现象。我们的工作以最简单的形式证明了事件崩溃，并通过使用基于差异几何和物理学的时空变形的第一原理提出了崩溃指标。我们通过实验表明，在公开可用的数据集上表明，拟议的指标减轻了事件崩溃，并且不会损害良好的扭曲。据我们所知，与其他方法相比，基于提议的指标的正规化器是唯一有效的解决方案，可以防止事件崩溃。我们希望这项工作激发了进一步的研究，以应对更复杂的翘曲模型。
Contrast maximization (CMax) is a framework that provides state-of-the-art results on several event-based computer vision tasks, such as ego-motion or optical flow estimation. However, it may suffer from a problem called event collapse, which is an undesired solution where events are warped into too few pixels. As prior works have largely ignored the issue or proposed workarounds, it is imperative to analyze this phenomenon in detail. Our work demonstrates event collapse in its simplest form and proposes collapse metrics by using first principles of space-time deformation based on differential geometry and physics. We experimentally show on publicly available datasets that the proposed metrics mitigate event collapse and do not harm well-posed warps. To the best of our knowledge, regularizers based on the proposed metrics are the only effective solution against event collapse in the experimental settings considered, compared with other methods. We hope that this work inspires further research to tackle more complex warp models.
