Predicting Perceptual Haptic Attributes of Textured Surface from Tactile Data Based on Deep CNN-LSTM Network-Reference-Cited by-同舟云学术

Predicting Perceptual Haptic Attributes of Textured Surface from Tactile Data Based on Deep CNN-LSTM Network

Published:2023-10-09 Issue: Volume: Page:
ISSN:
Container-title:29th ACM Symposium on Virtual Reality Software and Technology
language:
Short-container-title:

Author:

Awan Mudassir Ibrahim¹^ORCID,Hassan Waseem²^ORCID,Jeon Seokhee²^ORCID

Affiliation:

1. Haptics and Virtual Reality Laboratory, Kyung Hee University, South Korea

2. Haptics and Virtual Reality Laboratory, Kyung hee university, Korea, Republic of

Funder

Ministry of Science and ICT Korea under the ITRC support program

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3611659.3615714

Reference37 articles.

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2. Mudassir Ibrahim Awan , Tatyana Ogay , Waseem Hassan , Dongbeom Ko , Sungjoo Kang , and Seokhee Jeon . 2023. Model-Mediated Teleoperation for Remote Haptic Texture Sharing: Initial Study of Online Texture Modeling and Rendering. In 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, New York City, United States , 12457–12463. Mudassir Ibrahim Awan, Tatyana Ogay, Waseem Hassan, Dongbeom Ko, Sungjoo Kang, and Seokhee Jeon. 2023. Model-Mediated Teleoperation for Remote Haptic Texture Sharing: Initial Study of Online Texture Modeling and Rendering. In 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, New York City, United States, 12457–12463.

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4. Modeling and Rendering Realistic Textures from Unconstrained Tool-Surface Interactions

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