6D pose estimation and 3D object reconstruction from 2D shape for robotic grasping of objects-Reference-Cited by-同舟云学术

6D pose estimation and 3D object reconstruction from 2D shape for robotic grasping of objects

Published:2022-12 Issue: Volume: Page:
ISSN:
Container-title:2022 Sixth IEEE International Conference on Robotic Computing (IRC)
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Author:

Wolnitza Marcell¹,Kaya Osman¹,Kulvicius Tomas¹,Worgotter Florentin¹,Dellen Babette²

Affiliation:

1. Third Institute of Physics - Biophysics Georg-August-University Göttingen,Göttingen,Germany

2. University of Applied Sciences Koblenz,Faculty of Mathematics and Technology,Remagen,Germany

Publisher

IEEE

Link

Reference28 articles.

1. Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes;xiang;arXiv preprint arXiv 1711 00540,2017

2. Deep object pose estimation for semantic robotic grasping of household objects;tremblay;arXiv preprint arXiv 1809 10790,2018

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3. Pose prediction of textureless objects for robot bin picking with deep learning approach;Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science;2022-08-06