E-MOTION: A Dataset for Event-Based Scene Flow Estimation with Independent Moving Objects

Scene flow represents the 3D cartesian motion of points inthe world and can be used for applications such as navigation in dy-namic environments, human-robot interaction or non-rigid motion anal-ysis, among others. Despite the potential benefits of event cameras formotion estimation tasks, due to their high temporal resolution and low-latency, not many works have yet addressed their use for scene flowestimation. Progress may be limited by the unconventional data addingcomplexity to established processing pipelines, but also due to the lackof event camera datasets with scene flow ground truth. With the aimof filling this gap, we present E-MOTION: a large and versatile datasetrecorded with high-resolution event cameras suitable for depth, opticalflow and scene flow estimation. E-MOTION features a number of inde-pendent moving objects for which ground truth poses and segmentationmasks are also available. We release a total of 43 sequences with denseground truth maps and poses at 200 Hz. The dataset is available at:https://emotion.hds.utc.fr/