We investigate the problem of pixelwise correspondence for deformable
objects, namely cloth and rope, by comparing both classical and learning-based
methods. We choose cloth and rope because they are traditionally some of the
most difficult deformable objects to analytically model with their large
configuration space, and they are meaningful in the context of robotic tasks
like cloth folding, rope knot-tying, T-shirt folding, curtain closing, etc. The
correspondence problem is heavily motivated in robotics, with wide-ranging
applications including semantic grasping, object tracking, and manipulation
policies built on top of correspondences. We present an exhaustive survey of
existing classical methods for doing correspondence via feature-matching,
including SIFT, SURF, and ORB, and two recently published learning-based
methods including TimeCycle and Dense Object Nets. We make three main
contributions: (1) a framework for simulating and rendering synthetic images of
deformable objects, with qualitative results demonstrating transfer between our
simulated and real domains (2) a new learning-based correspondence method
extending Dense Object Nets, and (3) a standardized comparison across
state-of-the-art correspondence methods. Our proposed method provides a
flexible, general formulation for learning temporally and spatially continuous
correspondences for nonrigid (and rigid) objects. We report root mean squared
error statistics for all methods and find that Dense Object Nets outperforms
baseline classical methods for correspondence, and our proposed extension of
Dense Object Nets performs similarly.

我们研究了可变形物体（布料和绳索）的像素级对应问题，比较了传统方法和基于学习的方法。我们选择布料和绳索，因为它们在解析建模方面是最难的可变形物体之一，而且在机器人任务（如布料折叠、绳结打结、T 恤折叠、窗帘合拢等）中具有意义。我们提出了对于通过特征匹配进行对应的现有传统方法进行全面调查，包括 SIFT、SURF 和 ORB，以及两种最近发布的基于学习的方法，包括 TimeCycle 和 Dense Object Nets。我们的提出方法为学习非刚性（和刚性）物体的连续时间和空间对应提供了灵活、通用的公式。我们报告了所有方法的均方根误差统计结果，并发现 Dense Object Nets 在对应方面优于基准传统方法，我们提出的 Dense Object Nets 扩展方法表现类似。

学习可变形物体的对应关系

Learning Correspondence for Deformable Objects

What is the right object representation for manipulation? We would like
robots to visually perceive scenes and learn an understanding of the objects in
them that (i) is task-agnostic and can be used as a building block for a
variety of manipulation tasks, (ii) is generally applicable to both rigid and
non-rigid objects, (iii) takes advantage of the strong priors provided by 3D
vision, and (iv) is entirely learned from self-supervision. This is hard to
achieve with previous methods: much recent work in grasping does not extend to
grasping specific objects or other tasks, whereas task-specific learning may
require many trials to generalize well across object configurations or other
tasks. In this paper we present Dense Object Nets, which build on recent
developments in self-supervised dense descriptor learning, as a consistent
object representation for visual understanding and manipulation. We demonstrate
they can be trained quickly (approximately 20 minutes) for a wide variety of
previously unseen and potentially non-rigid objects. We additionally present
novel contributions to enable multi-object descriptor learning, and show that
by modifying our training procedure, we can either acquire descriptors which
generalize across classes of objects, or descriptors that are distinct for each
object instance. Finally, we demonstrate the novel application of learned dense
descriptors to robotic manipulation. We demonstrate grasping of specific points
on an object across potentially deformed object configurations, and demonstrate
using class general descriptors to transfer specific grasps across objects in a
class.

本文提出一种基于无监督学习的密集物体描述学习方法：Dense Object Nets，可用于视觉理解和操作，适用于针对刚性和非刚性物体的各种操作任务，并能够为多个物体类别提供通用描述。