Multi-object tracking from RGB-D video sequences is a challenging problem due
to the combination of changing viewpoints, motion, and occlusions over time. We
observe that having the complete geometry of objects aids in their tracking,
and thus propose to jointly infer the complete geometry of objects as well as
track them, for rigidly moving objects over time. Our key insight is that
inferring the complete geometry of the objects significantly helps in tracking.
By hallucinating unseen regions of objects, we can obtain additional
correspondences between the same instance, thus providing robust tracking even
under strong change of appearance. From a sequence of RGB-D frames, we detect
objects in each frame and learn to predict their complete object geometry as
well as a dense correspondence mapping into a canonical space. This allows us
to derive 6DoF poses for the objects in each frame, along with their
correspondence between frames, providing robust object tracking across the
RGB-D sequence. Experiments on both synthetic and real-world RGB-D data
demonstrate that we achieve state-of-the-art performance on dynamic object
tracking. Furthermore, we show that our object completion significantly helps
tracking, providing an improvement of $6.5\%$ in mean MOTA.

本文提出了一种基于 RGB-D 视频序列的多目标跟踪方法，通过联合推断目标的完整几何形状和跟踪它们来提高跟踪的稳健性，通过人工构建目标的视状图像，得到相同实例之间的额外对应关系，从而提高了跟踪的鲁棒性。实验结果表明该方法在动态目标跟踪方面取得了 state-of-the-art 的性能。

使用 RGB-D 序列在多个对象跟踪中看到物体背后

Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences

We present the first utterly self-supervised network for dense correspondence
mapping between non-isometric shapes. The task of alignment in non-Euclidean
domains is one of the most fundamental and crucial problems in computer vision.
As 3D scanners can generate highly complex and dense models, the mission of
finding dense mappings between those models is vital. The novelty of our
solution is based on a cyclic mapping between metric spaces, where the distance
between a pair of points should remain invariant after the full cycle. As the
same learnable rules that generate the point-wise descriptors apply in both
directions, the network learns invariant structures without any labels while
coping with non-isometric deformations. We show here state-of-the-art-results
by a large margin for a variety of tasks compared to known self-supervised and
supervised methods.

该研究提出了一个基于自我监督的网络，用于在非等度形状之间进行密集的对应映射，解决了在非欧几里得域中对齐的问题，相较于已知的自我监督和监督方法取得了最先进的结果。