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Notice of retraction
Vol. 34, No. 8(3), S&M3042

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 34, Number 1(3) (2022)
Copyright(C) MYU K.K.
pp. 303-317
S&M2812 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2022.3669
Published in advance: December 14, 2021
Published: January 31, 2022

Visual-perception-driven Urban Three-dimensional Scene Data Scheduling Method [PDF]

Xiang Wang, Tao Shen, Liang Huo, and Xiaoyong Zhang

(Received September 30, 2021; Accepted December 2, 2021)

Keywords: 3D scene data scheduling, level of detail, scene graph, geographical relationship, visual-perception evaluation model

Toward solving the problems of low data scheduling efficiency and relative delay in rendering when constructing complex urban three-dimensional (3D) scenes, we propose a visual-perception-driven strategy based on scene graphs. According to the spatial distribution characteristics of 3D scene data, this strategy uses a scene graph to organize the local 3D scene of a city. It uses a level-of-detail simplification algorithm to simplify the 3D model of the city into four resolution levels. On this basis, a visual-perception-driven strategy based on scene graphs is designed. This strategy utilizes the good relationship attributes between geographical entities provided by scene graphs to construct a visual-perception evaluation model and help constrain the adaptive scheduling of models at different detail levels. Experimental results show that this method can effectively improve the data scheduling efficiency and accelerate the construction of local 3D scenes.

Corresponding author: Tao Shen, Liang Huo


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Xiang Wang, Tao Shen, Liang Huo, and Xiaoyong Zhang, Visual-perception-driven Urban Three-dimensional Scene Data Scheduling Method, Sens. Mater., Vol. 34, No. 1, 2022, p. 303-317.



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