RL-LABEL: A Deep Reinforcement Learning Approach Intended for AR Label Placement in Dynamic Scenarios-Reference-Cited by-同舟云学术

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RL-LABEL: A Deep Reinforcement Learning Approach Intended for AR Label Placement in Dynamic Scenarios

Published:2024-01 Issue:1 Volume:30 Page:1347-1357
ISSN:1077-2626
Container-title:IEEE Transactions on Visualization and Computer Graphics
language:
Short-container-title:IEEE Trans. Visual. Comput. Graphics

Author:

Chen Zhutian¹^ORCID,Chiappalupi Daniele¹^ORCID,Lin Tica¹^ORCID,Yang Yalong²^ORCID,Beyer Johanna¹^ORCID,Pfister Hanspeter¹^ORCID

Affiliation:

1. Harvard John A. Paulson School of Engineering and Applied Sciences, United States

2. Virginia Tech, United States

Funder

NSF

NIH

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

http://xplorestaging.ieee.org/ielx7/2945/10373160/10290983.pdf?arnumber=10290983

Reference58 articles.

1. Part 3: The Merger of Computer Data and Thematic Mapping: A Program For Automatic Name Placement

2. Evaluating label placement for augmented reality view management

3. External Labeling Techniques: A Taxonomy and Survey

4. A Multi-Level Typology of Abstract Visualization Tasks

5. Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning

Cited by 1 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. Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning;Extended Abstracts of the CHI Conference on Human Factors in Computing Systems;2024-05-11

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