  Object grasping in cluttered scenes is a widely investigated field of robot
manipulation. Most of the current works focus on estimating grasp pose from
point clouds based on an efficient single-shot grasp detection network.
However, due to the lack of geometry awareness of the local grasping area, it
may cause severe collisions and unstable grasp configurations. In this paper,
we propose a two-stage grasp pose refinement network which detects grasps
globally while fine-tuning low-quality grasps and filtering noisy grasps
locally. Furthermore, we extend the 6-DoF grasp with an extra dimension as
grasp width which is critical for collisionless grasping in cluttered scenes.
It takes a single-view point cloud as input and predicts dense and precise
grasp configurations. To enhance the generalization ability, we build a
synthetic single-object grasp dataset including 150 commodities of various
shapes, and a multi-object cluttered scene dataset including 100k point clouds
with robust, dense grasp poses and mask annotations. Experiments conducted on
Yumi IRB-1400 Robot demonstrate that the model trained on our dataset performs
well in real environments and outperforms previous methods by a large margin.
