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Found 7 papers, 3 papers with code
Graspness Discovery in Clutters for Fast and Accurate Grasp Detection
Experiments on a large-scale benchmark, GraspNet-1Billion, show that our method outperforms previous arts by a large margin (30+ AP) and achieves a high inference speed.
Ranked #3 on
Robotic Grasping
on GraspNet-1Billion
Target-Referenced Reactive Grasping for Dynamic Objects
Reactive grasping, which enables the robot to successfully grasp dynamic moving objects, is of great interest in robotics.
Unseen Object 6D Pose Estimation: A Benchmark and Baselines
In this paper, we propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing.
A Real World Dataset for Multi-view 3D Reconstruction
We present a dataset of 998 3D models of everyday tabletop objects along with their 847, 000 real world RGB and depth images.
RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images
In the first stage, an encoder-decoder like convolutional neural network Angle-View Net(AVN) is proposed to predict the SO(3) orientation of the gripper at every location of the image.
GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping
Object grasping is critical for many applications, which is also a challenging computer vision problem.
InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting
With the guidance of such map, we boost the performance of R101-Mask R-CNN on instance segmentation from 35. 7 mAP to 37. 9 mAP without modifying the backbone or network structure.
Ranked #80 on
Instance Segmentation
on COCO test-dev
