论文标题
半自动3D对象关键点注释和质量检测
Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses
论文作者
论文摘要
创建计算机视觉数据集需要仔细的计划以及大量的时间和精力。在机器人研究中，我们通常必须使用标准化对象，例如YCB对象集，例如对象跟踪，姿势估计，抓握和操纵，因为有数据集和预测方法可用于这些对象。这限制了我们研究的影响，因为基于学习的计算机视觉方法只能在现有数据集支持的情况下使用。 在这项工作中，我们提供了一个完整的对象关键点跟踪工具包，其中包括数据收集，标签，模型学习和评估的整个过程。我们提出了一种半自动的方式，可以在标准机器人臂上使用手腕安装的摄像头收集和标记数据集。使用我们的工具包和方法，我们能够获得一个有效的3D对象键盘检测器，并在仅几个小时的活动时间内完成数据收集，注释和学习的整个过程。
Creating computer vision datasets requires careful planning and lots of time and effort. In robotics research, we often have to use standardized objects, such as the YCB object set, for tasks such as object tracking, pose estimation, grasping and manipulation, as there are datasets and pre-learned methods available for these objects. This limits the impact of our research since learning-based computer vision methods can only be used in scenarios that are supported by existing datasets. In this work, we present a full object keypoint tracking toolkit, encompassing the entire process from data collection, labeling, model learning and evaluation. We present a semi-automatic way of collecting and labeling datasets using a wrist mounted camera on a standard robotic arm. Using our toolkit and method, we are able to obtain a working 3D object keypoint detector and go through the whole process of data collection, annotation and learning in just a couple hours of active time.
