Liu, L, Yang, Y, Shao, T et al. (2 more authors) (2021) In-game Residential Home Planning via Visual Context-aware Global Relation Learning. In: AAAI-21 Technical Tracks 1. Thirty-Fifth AAAI Conference on Artificial Intelligence, 02-09 Feb 2021, Online. Association for the Advancement of Artificial Intelligence , pp. 336-343. ISBN 978-1-57735-866-4
Abstract
In this paper, we propose an effective global relation learning algorithm to recommend an appropriate location of a building unit for in-game customization of residential home complex. Given a construction layout, we propose a visual context-aware graph generation network that learns the implicit global relations among the scene components and infers the location of a new building unit. The proposed network takes as input the scene graph and the corresponding top-view depth image. It provides the location recommendations for a newly added building units by learning an auto-regressive edge distribution conditioned on existing scenes. We also introduce a global graph-image matching loss to enhance the awareness of essential geometry semantics of the site. Qualitative and quantitative experiments demonstrate that the recommended location well reflects the implicit spatial rules of components in the residential estates, and it is instructive and practical to locate the building units in the 3D scene of the complex construction.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Mar 2021 16:18 |
Last Modified: | 06 Dec 2021 16:15 |
Published Version: | https://ojs.aaai.org/index.php/AAAI/article/view/1... |
Status: | Published |
Publisher: | Association for the Advancement of Artificial Intelligence |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170233 |