We present 6-PACK, a deep learning approach to category-level 6D object pose
tracking on RGB-D data. Our method tracks in real-time novel object instances
of known object categories such as bowls, laptops, and mugs. 6-PACK learns to
compactly represent an object by a handful of 3D keypoints, based on which the
interframe motion of an object instance can be estimated through keypoint
matching. These keypoints are learned end-to-end without manual supervision in
order to be most effective for tracking. Our experiments show that our method
substantially outperforms existing methods on the NOCS category-level 6D pose
estimation benchmark and supports a physical robot to perform simple
vision-based closed-loop manipulation tasks. Our code and video are available
at this https URL

使用 6-PACK 这种基于深度学习的方法，我们可以针对已知对象类别如碗、笔记本电脑和马克杯，在 RGB-D 数据上实时跟踪新物体实例的类别级 6D 目标位姿。通过无需手动监督的方式学习关键点的方法，可以压缩表示物体，并在其基础上进行跟踪，同时它也可以支持机器人执行简单的基于视觉的闭环操作任务。