PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds-Reference-Cited by-同舟云学术

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PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds

Published:2020-05 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE International Conference on Robotics and Automation (ICRA)
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Author:

Ni Peiyuan,Zhang Wenguang,Zhu Xiaoxiao,Cao Qixin

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9187508/9196508/09196740.pdf?arnumber=9196740

Cited by 90 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Learning accurate and efficient three-finger grasp generation in clutters with an auto-annotated large-scale dataset;Robotics and Computer-Integrated Manufacturing;2025-02

2. Sim-Grasp: Learning 6-DOF Grasp Policies for Cluttered Environments Using a Synthetic Benchmark;IEEE Robotics and Automation Letters;2024-09

3. TransFusion: Multi-Modal Robust Fusion for 3D Object Detection in Foggy Weather Based on Spatial Vision Transformer;IEEE Transactions on Intelligent Transportation Systems;2024-09

4. A 3D pickup estimation method for industrial parallel robots based on point cloud simplification and registration;The International Journal of Advanced Manufacturing Technology;2024-07-02

5. Robot Grasp in Cluttered Scene Using a Multi-Stage Deep Learning Model;IEEE Robotics and Automation Letters;2024-07

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