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 this https URL

我们的研究引入了 YCB-Ev 数据集，该数据集包含了同步的 RGB-D 帧和事件数据，可以评估使用这些模态的六自由度物体姿态估计算法。

YCB-Ev：用于 6DoF 物体姿态估计的事件视觉数据集

YCB-Ev: Event-vision dataset for 6DoF object pose estimation

Detecting objects and estimating their 6D poses is essential for automated
systems to interact safely with the environment. Most 6D pose estimators,
however, rely on a single camera frame and suffer from occlusions and
ambiguities due to object symmetries. We overcome this issue by presenting a
novel symmetry-aware multi-view 6D pose estimator called SyMFM6D. Our approach
efficiently fuses the RGB-D frames from multiple perspectives in a deep
multi-directional fusion network and predicts predefined keypoints for all
objects in the scene simultaneously. Based on the keypoints and an instance
semantic segmentation, we efficiently compute the 6D poses by least-squares
fitting. To address the ambiguity issues for symmetric objects, we propose a
novel training procedure for symmetry-aware keypoint detection including a new
objective function. Our SyMFM6D network significantly outperforms the
state-of-the-art in both single-view and multi-view 6D pose estimation. We
furthermore show the effectiveness of our symmetry-aware training procedure and
demonstrate that our approach is robust towards inaccurate camera calibration
and dynamic camera setups.

该论文介绍了一种新的对物体进行 6D 姿态估计的方法，其中使用了多视角深度网络、预定义关键点、实例语义分割等技术，特别考虑了对称物体的问题，相对于现有技术有很大的提升。