Context-aware mixed reality: A learning-based framework for semantic-level interaction
View/ Open
Wan_et_al-2020-Computer_Graphics_Forum.pdf (2.228Mb)
Download
Publication date
2020-02Keyword
Interaction techniquesInteraction
Methods and applications‐computer games
Methods and applications
Augmented reality
Virtual environments
Rights
© 2019 The Authors. Computer Graphics Forum published by Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Peer-Reviewed
Yes
Metadata
Show full item recordAbstract
Mixed reality (MR) is a powerful interactive technology for new types of user experience. We present a semantic‐based interactive MR framework that is beyond current geometry‐based approaches, offering a step change in generating high‐level context‐aware interactions. Our key insight is that by building semantic understanding in MR, we can develop a system that not only greatly enhances user experience through object‐specific behaviours, but also it paves the way for solving complex interaction design challenges. In this paper, our proposed framework generates semantic properties of the real‐world environment through a dense scene reconstruction and deep image understanding scheme. We demonstrate our approach by developing a material‐aware prototype system for context‐aware physical interactions between the real and virtual objects. Quantitative and qualitative evaluation results show that the framework delivers accurate and consistent semantic information in an interactive MR environment, providing effective real‐time semantic‐level interactions.Version
Published versionCitation
Chen L, Tang W, John NW et al (2020) Context-aware mixed reality: A learning-based framework for semantic-level interaction. Computer Graphics Forum. 39(1): 484-496.Link to Version of Record
https://doi.org/10.1111/cgf.13887Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.1111/cgf.13887