Trilateral convolutional neural network for 3D shape reconstruction of objects from a single depth view-Reference-Cited by-同舟云学术

Trilateral convolutional neural network for 3D shape reconstruction of objects from a single depth view

Published:2019-10-10 Issue:13 Volume:13 Page:2457-2466
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET image process

Author:

Rivera Patricio¹,Valarezo Añazco Edwin¹,Choi Mun‐Taek²,Kim Tae‐Seong¹

Affiliation:

1. Department of Biomedical EngineeringKyung Hee UniversityYonginRepublic of Korea

2. School of Mechanical Engineering, Sungkyunkwan UniversitySuwonRepublic of Korea

Funder

Ministry of Education, Science and Technology

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2019.0532

Reference29 articles.

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2. 2018 The IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) Salt Lake City USA A. Kundu Y. Li J.M. Rehg 3D‐RCNN: instance‐level 3D object reconstruction via render‐and‐compare 3559 3568

3. 2017 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) Vancouver Canada J. Varley C. DeChant A. Richardson Shape completion enabled robotic grasping 2442 2447

4. Watkins‐VallsD. VarleyJ. andAllenP.: ‘Multi‐modal geometric learning for grasping and manipulation’. in arXiv e‐prints 2018 arXiv:1803.07671

5. 2018 European Conf. on Computer Vision Munich Germany M. Simon S. Milz K. Amende Complex‐YOLO: an Euler‐region‐proposal for real‐time 3D object detection on point clouds 197 209

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