Our work introduces the YCB-Ev dataset, which contains synchronized RGB-D frames and event data that enables evaluating 6DoF object pose estimation algorithms using these modalities. This dataset provides ground truth 6DoF object poses for the same 21 YCB objects \cite{calli2017yale} that were used in the YCB-Video (YCB-V) dataset, enabling the evaluation of algorithm performance when transferred across datasets. The dataset consists of 21 synchronized event and RGB-D sequences, amounting to a total of 7:43 minutes of video. Notably, 12 of these sequences feature the same object arrangement as the YCB-V subset used in the BOP challenge. Our dataset is the first to provide ground truth 6DoF pose data for event streams. Furthermore, we evaluate the generalization capabilities of two state-of-the-art algorithms, which were pre-trained for the BOP challenge, using our novel YCB-V sequences. The proposed dataset is available at https://github.com/paroj/ycbev.

翻译：我们的工作介绍了YCB-Ev数据集，该数据集包含同步的RGB-D帧与事件数据，能够评估基于这些模态的6自由度物体姿态估计算法。该数据集为YCB-Video（YCB-V）数据集中使用的同一组21个YCB物体\cite{calli2017yale}提供了真实6自由度物体姿态，从而可在跨数据集迁移时评估算法性能。数据集由21段同步事件与RGB-D序列组成，总时长为7分43秒。值得注意的是，其中12段序列与BOP挑战赛中使用的YCB-V子集具有相同的物体布局。本数据集是首个为事件流提供真实6自由度姿态数据的数据集。此外，我们使用全新的YCB-V序列评估了两项为BOP挑战赛预训练的先进算法的泛化能力。所提出数据集可从https://github.com/paroj/ycbev获取。