  This paper addresses the challenge of robotic grasping of general objects.
Similar to prior research, the task reads a single-view 3D observation (i.e.,
point clouds) captured by a depth camera as input. Crucially, the success of
object grasping highly demands a comprehensive understanding of the shape of
objects within the scene. However, single-view observations often suffer from
occlusions (including both self and inter-object occlusions), which lead to
gaps in the point clouds, especially in complex cluttered scenes. This renders
incomplete perception of the object shape and frequently causes failures or
inaccurate pose estimation during object grasping. In this paper, we tackle
this issue with an effective albeit simple solution, namely completing
grasping-related scene regions through local occupancy prediction. Following
prior practice, the proposed model first runs by proposing a number of most
likely grasp points in the scene. Around each grasp point, a module is designed
to infer any voxel in its neighborhood to be either void or occupied by some
object. Importantly, the occupancy map is inferred by fusing both local and
global cues. We implement a multi-group tri-plane scheme for efficiently
aggregating long-distance contextual information. The model further estimates
6-DoF grasp poses utilizing the local occupancy-enhanced object shape
information and returns the top-ranked grasp proposal. Comprehensive
experiments on both the large-scale GraspNet-1Billion benchmark and real
robotic arm demonstrate that the proposed method can effectively complete the
unobserved parts in cluttered and occluded scenes. Benefiting from the
occupancy-enhanced feature, our model clearly outstrips other competing methods
under various performance metrics such as grasping average precision.
