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.

翻译：我们通过比较传统方法与基于学习的方法，研究了可变形物体（即布料和绳索）的像素级对应点匹配问题。选择布料和绳索作为研究对象，是因为它们传统上属于最难通过解析建模的可变形物体（其构型空间巨大），且与机器人任务（如布料折叠、绳索打结、T恤折叠、窗帘闭合等）密切相关。对应点匹配问题在机器人领域具有高度应用价值，涵盖语义抓取、物体跟踪以及基于对应点的操作策略等广泛场景。我们对现有基于特征匹配的传统方法（包括SIFT、SURF和ORB）以及近期发表的两种基于学习的方法（TimeCycle和Dense Object Nets）进行了系统性综述。本文主要贡献有三：（1）建立可变形物体合成图像仿真与渲染框架，通过定性结果验证仿真域与真实域之间的迁移能力；（2）提出一种扩展Dense Object Nets的基于学习的新对应点匹配方法；（3）对当前最先进的对应点匹配方法进行标准化比较。我们提出的方法为非刚性（及刚性）物体提供了灵活通用的时空连续对应点学习框架。所有方法的均方根误差统计结果表明，Dense Object Nets在对应点匹配任务上优于传统基线方法，而我们所提出的扩展方案性能与Dense Object Nets相当。