  A significant challenge for real-world robotic manipulation is the effective
6DoF grasping of objects in cluttered scenes from any single viewpoint without
the need for additional scene exploration. This work reinterprets grasping as
rendering and introduces NeuGraspNet, a novel method for 6DoF grasp detection
that leverages advances in neural volumetric representations and surface
rendering. It encodes the interaction between a robot's end-effector and an
object's surface by jointly learning to render the local object surface and
learning grasping functions in a shared feature space. The approach uses global
(scene-level) features for grasp generation and local (grasp-level) neural
surface features for grasp evaluation. This enables effective, fully implicit
6DoF grasp quality prediction, even in partially observed scenes. NeuGraspNet
operates on random viewpoints, common in mobile manipulation scenarios, and
outperforms existing implicit and semi-implicit grasping methods. The
real-world applicability of the method has been demonstrated with a mobile
manipulator robot, grasping in open, cluttered spaces. Project website at
https://sites.google.com/view/neugraspnet
