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 HuoThis 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. |