YCB-Ev: Event-vision dataset for 6DoF object pose estimation
Pavel Rojtberg Thomas Pöllabauer
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.