MonoGraspNet: 6-DoF Grasping with a Single RGB Image
Author:
Affiliation:
1. Technical University of Munich (TUM),Munich,Germany
2. Google.
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10160211/10160212/10160779.pdf?arnumber=10160779
Reference51 articles.
1. GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping
2. Grasp proposal networks: an end-to-end solution for visual learning of robotic grasps;wu;NeurIPS,2020
3. Project to adapt: Domain adaptation for depth completion from noisy and sparse sensor data;lopez-rodriguez;ACCV,2020
4. On the importance of accurate geometry priors for dense 3d vision tasks;jung;CVPR,2023
5. 6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
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1. GaussianGrasper: 3D Language Gaussian Splatting for Open-Vocabulary Robotic Grasping;IEEE Robotics and Automation Letters;2024-09
2. RGBGrasp: Image-Based Object Grasping by Capturing Multiple Views During Robot arm Movement With Neural Radiance Fields;IEEE Robotics and Automation Letters;2024-06
3. SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
4. NeRF-Feat: 6D Object Pose Estimation using Feature Rendering;2024 International Conference on 3D Vision (3DV);2024-03-18
5. NFL: Normal Field Learning for 6-DoF Grasping of Transparent Objects;IEEE Robotics and Automation Letters;2024-01
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