Contrast maximization (CMax) techniques are widely used in event-based vision
systems to estimate the motion parameters of the camera and generate
high-contrast images. However, these techniques are noise-intolerance and
suffer from the multiple extrema problem which arises when the scene contains
more noisy events than structure, causing the contrast to be higher at multiple
locations. This makes the task of estimating the camera motion extremely
challenging, which is a problem for neuromorphic earth observation, because,
without a proper estimation of the motion parameters, it is not possible to
generate a map with high contrast, causing important details to be lost.
Similar methods that use CMax addressed this problem by changing or augmenting
the objective function to enable it to converge to the correct motion
parameters. Our proposed solution overcomes the multiple extrema and
noise-intolerance problems by correcting the warped event before calculating
the contrast and offers the following advantages: it does not depend on the
event data, it does not require a prior about the camera motion, and keeps the
rest of the CMax pipeline unchanged. This is to ensure that the contrast is
only high around the correct motion parameters. Our approach enables the
creation of better motion-compensated maps through an analytical compensation
technique using a novel dataset from the International Space Station (ISS).
Code is available at https://github.com/neuromorphicsystems/event_warping

本文提出的事件在计算对比度之前校正，从而克服了具有多个极值问题和噪声容忍度问题的对比度最大化技术应用于事件感知的挑战，并使用来自国际空间站的新数据集，通过分析的补偿技术实现更好的运动补偿映射。