Search Results for author: Minghao Gou

Found 7 papers, 3 papers with code

Target-Referenced Reactive Grasping for Dynamic Objects

no code implementations CVPR 2023 Jirong Liu, Ruo Zhang, Hao-Shu Fang, Minghao Gou, Hongjie Fang, Chenxi Wang, Sheng Xu, Hengxu Yan, Cewu Lu

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

no code implementations23 Jun 2022 Minghao Gou, Haolin Pan, Hao-Shu Fang, Ziyuan Liu, Cewu Lu, Ping Tan

In this paper, we propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing.

6D Pose Estimation

A Real World Dataset for Multi-view 3D Reconstruction

no code implementations22 Mar 2022 Rakesh Shrestha, Siqi Hu, Minghao Gou, Ziyuan Liu, Ping Tan

We present a dataset of 998 3D models of everyday tabletop objects along with their 847, 000 real world RGB and depth images.

3D Reconstruction Multi-View 3D Reconstruction +3

RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images

1 code implementation3 Mar 2021 Minghao Gou, Hao-Shu Fang, Zhanda Zhu, Sheng Xu, Chenxi Wang, Cewu Lu

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

1 code implementation ICCV 2021 Chenxi Wang, Hao-Shu Fang, Minghao Gou, Hongjie Fang, Jin Gao, Cewu Lu

To quickly detect graspness in practice, we develop a neural network named graspness model to approximate the searching process.

Robotic Grasping

GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping

no code implementations31 Dec 2019 Hao-Shu Fang, Chenxi Wang, Minghao Gou, Cewu Lu

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

3 code implementations ICCV 2019 Hao-Shu Fang, Jianhua Sun, Runzhong Wang, Minghao Gou, Yong-Lu Li, Cewu Lu

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.

Data Augmentation Instance Segmentation +3

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