TL;DR: We proposed an analytical solution that addresses the problem of motion estimation under extremely noisy conditions. Our method removes the peak in the landscape caused by the high density noise and only keep the peak produced by the motion in the scene without prior knowledge of the motion. This allows for more accurate earth mapping and observations using data from the International Space Station using an event camera.
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).
@InProceedings{arjaDensityInvariantCMax2023,
author = {Arja, Sami and Marcireau, Alexandre and Balthazor, Richard L. and McHarg, Matthew G. and Afshar, Saeed and Cohen, Gregory},
title = {Density Invariant Contrast Maximization for Neuromorphic Earth Observations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2023},
pages = {3983-3993}
}