We propose a novel Dispersion Minimisation framework for event-based vision model estimation, with applications to optical flow and high-speed motion estimation. The framework extends previous event-based motion compensation algorithms by avoiding computing an optimisation score based on an explicit image-based representation, which provides three main benefits: i) The framework can be extended to perform incremental estimation, i.e., on an event-by-event basis. ii) Besides purely visual transformations in 2D, the framework can readily use additional information, e.g., by augmenting the events with depth, to estimate the parameters of motion models in higher dimensional spaces. iii) The optimisation complexity only depends on the number of events. We achieve this by modelling the event alignment according to candidate parameters and minimising the resultant dispersion, which is computed by a family of suitable entropy-based measures. Data whitening is also proposed as a simple and effective pre-processing step to make the framework’s accuracy performance more robust, as well as other event-based motion-compensation methods. The framework is evaluated on several challenging motion estimation problems, including 6-DOF transformation, rotational motion, and optical flow estimation, achieving state-of-the-art performance.

中文翻译：

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通过色散最小化进行鲁棒的基于事件的视觉模型估计


我们提出了一种新颖的色散最小化框架，用于基于事件的视觉模型估计，并应用于光流和高速运动估计。该框架通过避免基于显式基于图像的表示计算优化分数来扩展以前的基于事件的运动补偿算法，这提供了三个主要好处： i) 该框架可以扩展以执行增量估计，即基于事件- 事件基础。 ii）除了纯粹的二维视觉变换之外，该框架还可以轻松地使用附加信息，例如通过深度增强事件来估计更高维空间中运动模型的参数。 iii) 优化复杂度仅取决于事件的数量。我们通过根据候选参数对事件对齐进行建模并最小化由此产生的离散度来实现这一点，离散度是通过一系列合适的基于熵的度量来计算的。数据白化也被提出作为一种简单而有效的预处理步骤，以使框架的准确性性能更加稳健，以及其他基于事件的运动补偿方法。该框架针对几个具有挑战性的运动估计问题进行了评估，包括六自由度变换、旋转运动和光流估计，实现了最先进的性能。