Taking advantage of an event-based camera, the issues of motion blur, low dynamic range and low time sampling of standard cameras can all be addressed. However, there is a lack of event-based datasets dedicated to the benchmarking of segmentation algorithms, especially those that provide depth information which is critical for segmentation in occluded scenes. This paper proposes a new Event-based Segmentation Dataset (ESD), a high-quality 3D spatial and temporal dataset for object segmentation in an indoor cluttered environment. Our proposed dataset ESD comprises 145 sequences with 14,166 RGB frames that are manually annotated with instance masks. Overall 21.88 million and 20.80 million events from two event-based cameras in a stereo-graphic configuration are collected, respectively. To the best of our knowledge, this densely annotated and 3D spatial-temporal event-based segmentation benchmark of tabletop objects is the first of its kind. By releasing ESD, we expect to provide the community with a challenging segmentation benchmark with high quality.

翻译：利用事件相机，可以解决标准相机的运动模糊、低动态范围和低时间采样等问题。然而，目前缺乏专门用于分割算法基准测试的事件数据集，尤其是那些提供对遮挡场景分割至关重要的深度信息的数据集。本文提出了一种新的事件分割数据集（ESD），这是一个用于室内杂乱环境中物体分割的高质量三维时空数据集。我们提出的ESD数据集包含145个序列，共14,166帧RGB图像，这些图像均带有实例掩码的手动标注。从立体配置的两个事件相机中，分别采集了约2188万和2080万个事件。据我们所知，这是首个密集标注的、三维时空事件桌面物体分割基准数据集。通过发布ESD，我们期望为社区提供一个高质量且具有挑战性的分割基准。