We present FuncGrasp, a framework that can infer dense yet reliable grasp
configurations for unseen objects using one annotated object and single-view
RGB-D observation via categorical priors. Unlike previous works that only
transfer a set of grasp poses, FuncGrasp aims to transfer infinite
configurations parameterized by an object-centric continuous grasp function
across varying instances. To ease the transfer process, we propose Neural
Surface Grasping Fields (NSGF), an effective neural representation defined on
the surface to densely encode grasp configurations. Further, we exploit
function-to-function transfer using sphere primitives to establish semantically
meaningful categorical correspondences, which are learned in an unsupervised
fashion without any expert knowledge. We showcase the effectiveness through
extensive experiments in both simulators and the real world. Remarkably, our
framework significantly outperforms several strong baseline methods in terms of
density and reliability for generated grasps.

FuncGrasp 是一个框架，可以通过一个带有注释的对象和单视图 RGB-D 观察来推断未见过的物体的密集而可靠的抓取配置，通过球形基元的函数到函数传递建立语义有意义的分类对应关系，并在无监督学习的方式下学习。与以往的作品不同，FuncGrasp 旨在传输通过对象为中心的连续抓握函数参数化的无限配置，通过在表面上定义的有效神经表示（NSGF）密集编码抓握配置。通过在模拟器和真实世界中进行大量实验，我们展示了该方法的有效性，显着优于生成的抓握的密度和可靠性的几种强基准方法。