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:利用以事件为基础的照相机,可以解决标准照相机的运动模糊、低动态范围和低时间抽样等问题,然而,缺乏专门为分层算法基准设定的基于事件的数据集,特别是提供对隐蔽场景分解至关重要的深度信息的数据集。本文提议建立一个新的基于事件的分层数据集(ESD),这是用于室内隔热环境中物体分解的高质量3D空间和时间数据集。我们提议的数据集ESD由145个序列组成,其中14,166 RGB框架是人工加掩体说明的14,166 RGB框架。总共收集了2,188万个和2,080万个事件,分别来自两台立体图结构的基于事件的相机。据我们所知,这种高密度的附加说明和3D空间-时空分解基准是桌面物体的首个类型。通过释放ESD,我们期望向社区提供具有挑战性的分解基准,质量很高。