Context 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.

翻译：环境最大化(Cmax)是一个框架,它为一些以事件为基础的计算机视觉任务(如自我感动或光学流量估计)提供了最先进的结果。然而,它可能遭遇一个被称为事件崩溃的问题,这是一个不理想的解决方案,事件被扭曲成太少的像素。由于先前的工程基本上忽视了问题或拟议的变通办法,因此必须详细分析这一现象。我们的工作以最简单的形式显示了事件崩溃,并通过使用基于不同几何和物理的空间时间变形的第一条原则提出崩溃度量。我们实验性地在公开的数据集中显示,拟议的衡量标准可以减轻事件崩溃,而不会伤害到好坏的扭曲。据我们所知,与其它方法相比,基于拟议指标的正规化者是应对所考虑的实验环境中事件崩溃的唯一有效解决办法。我们希望这项工作能够激励进一步研究更复杂的扭曲模型。