6 citations · 11 across the 2 of their papers we have counts for
4 papers
OCRTOC: A Cloud-Based Competition and Benchmark for Robotic Grasping and Manipulation
Ziyuan Liu, Wei Liu, Yuzhe Qin +8
In this paper, we propose a cloud-based benchmark for robotic grasping and manipulation, called the OCRTOC benchmark. The benchmark focuses on the object rearrangement problem, spe…
RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images
Minghao Gou, Hao-Shu Fang, Zhanda Zhu +3
General object grasping is an important yet unsolved problem in the field of robotics. Most of the current methods either generate grasp poses with few DoF that fail to cover most…
GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping
Hao-Shu Fang, Chenxi Wang, Minghao Gou +1
Object grasping is critical for many applications, which is also a challenging computer vision problem. However, for the clustered scene, current researches suffer from the problem…
InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting
Hao-Shu Fang, Jianhua Sun, Runzhong Wang +3
Instance segmentation requires a large number of training samples to achieve satisfactory performance and benefits from proper data augmentation. To enlarge the training set and in…