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Found 7 papers, 3 papers with code
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.
Graspness Discovery in Clutters for Fast and Accurate Grasp Detection
To quickly detect graspness in practice, we develop a neural network named graspness model to approximate the searching process.
Ranked #3 on
Robotic Grasping
on GraspNet-1Billion
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 #78 on
Instance Segmentation
on COCO test-dev
