论文标题
YCB-M：用于对象识别和6DOF姿势估计的多相机RGB-D数据集
YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation
论文作者
论文摘要
虽然近年来已经引入了各种各样的3D摄像机，但大多数可公开可用的数据集用于对象识别，并构成估算的侧重于一台摄像机。在这项工作中，我们介绍了32个场景的数据集，该数据集由7种不同的3D摄像机捕获，总计49,294帧。这允许评估姿势估计算法对使用相机的细节的敏感性，以及更与摄像机模型更独立的更强大算法的开发。反之亦然，我们的数据集使研究人员可以对来自几个不同相机和深度传感技术的数据进行定量比较，并在为其特定任务选择相机之前评估其算法。我们数据集中的场景包含20个不同的对象，来自常见的基准YCB对象和模型集[1]，[2]。我们为每个对象，每个像素分割，2D和3D边界框以及每个对象的遮挡量的度量提供完整的地面真相6DOF姿势。我们还使用数据集对最新的对象识别和姿势估计系统进行了对摄像机的初步评估[3]。
While a great variety of 3D cameras have been introduced in recent years, most publicly available datasets for object recognition and pose estimation focus on one single camera. In this work, we present a dataset of 32 scenes that have been captured by 7 different 3D cameras, totaling 49,294 frames. This allows evaluating the sensitivity of pose estimation algorithms to the specifics of the used camera and the development of more robust algorithms that are more independent of the camera model. Vice versa, our dataset enables researchers to perform a quantitative comparison of the data from several different cameras and depth sensing technologies and evaluate their algorithms before selecting a camera for their specific task. The scenes in our dataset contain 20 different objects from the common benchmark YCB object and model set [1], [2]. We provide full ground truth 6DoF poses for each object, per-pixel segmentation, 2D and 3D bounding boxes and a measure of the amount of occlusion of each object. We have also performed an initial evaluation of the cameras using our dataset on a state-of-the-art object recognition and pose estimation system [3].
