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。

基于锚点的类别级别 6D 姿态追踪器

6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints

Tracking 6D poses of objects from videos provides rich information to a robot
in performing different tasks such as manipulation and navigation. In this
work, we formulate the 6D object pose tracking problem in the Rao-Blackwellized
particle filtering framework, where the 3D rotation and the 3D translation of
an object are decoupled. This factorization allows our approach, called
PoseRBPF, to efficiently estimate the 3D translation of an object along with
the full distribution over the 3D rotation. This is achieved by discretizing
the rotation space in a fine-grained manner, and training an auto-encoder
network to construct a codebook of feature embeddings for the discretized
rotations. As a result, PoseRBPF can track objects with arbitrary symmetries
while still maintaining adequate posterior distributions. Our approach achieves
state-of-the-art results on two 6D pose estimation benchmarks. A video showing
the experiments can be found at this https URL

该文提出了一种基于 Rao-Blackwellized 粒子滤波方法的 6D 目标姿态跟踪算法 PoseRBPF，该算法通过旋转和平移的分解实现了对对象 3D Translation 的有效估计，并且在对旋转空间精细离散方式下进行了特征嵌入的自编码神经网络的训练以获得特征嵌入代码本，以实现对任意对称对象的跟踪并仍保持足够的后验分布。该方法在两个 6D 姿态估计基准测试上实现了最先进的结果。