Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Context-consistent generation of indoor virtual environments based on geometry constraints

He, Yu, Liu, Yingtian, Jin, Yihan, Zhang, Song-Hai, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680 and Hu, Shi-Min 2022. Context-consistent generation of indoor virtual environments based on geometry constraints. IEEE Transactions on Visualization and Computer Graphics 28 (12) , pp. 3986-3999. 10.1109/TVCG.2021.3111729

[thumbnail of GeoConsGenVR_TVCG.pdf] PDF - Accepted Post-Print Version
Download (32MB)

Abstract

In this paper, we propose a system that can automatically generate immersive and interactive virtual reality (VR) scenes by taking real-world geometric constraints into account. Our system can not only help users avoid real-world obstacles in virtual reality experiences, but also provide context-consistent contents to preserve their sense of presence. To do so, our system first identifies the positions and bounding boxes of scene objects as well as a set of interactive planes from 3D scans. Then context-compatible virtual objects that have similar geometric properties to the real ones can be automatically selected and placed into the virtual scene, based on learned object association relations and layout patterns from large amounts of indoor scene configurations. We regard virtual object replacement as a combinatorial optimization problem, considering both geometric and contextual consistency constraints. Quantitative and qualitative results show that our system can generate plausible interactive virtual scenes that highly resemble real environments, and have the ability to keep the sense of presence for users in their VR experiences.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1077-2626
Date of First Compliant Deposit: 14 September 2021
Date of Acceptance: 28 August 2021
Last Modified: 01 Dec 2024 15:45
URI: https://orca.cardiff.ac.uk/id/eprint/144079

Citation Data

Cited 6 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics