{"title":"3D urban landscape rendering and optimization algorithm for smart city","authors":"Li Wang","doi":"10.3233/idt-230418","DOIUrl":null,"url":null,"abstract":"3D urban landscape visualization is a key technology in digital city construction. Based on the research and analysis of the three-dimensional space of the urban landscape space, the three-dimensional space can not only allow users to intuitively perceive the development of the city. It also enables decision makers, planners, and users to more intuitively, objectively, and rationally recognize and understand the current urban development and planning design. Defining the data content of the 3D city landscape image model is the basis for creating the 3D city image model. It not only guides producers to select data, but also serves as the basis for sharing data between different applications. With the continuous development of society, the number of people living in rural areas migrating to cities to make a living has increased rapidly, leading to the growing problem of “urban congestion” in many areas. In order to effectively solve these problems, “smart cities” came into being. It quickly triggered a boom in global urban development. Based on a survey of the state-of-the-art in the field of 3D modeling and engineering design visualization, this paper analyzes 3D rendering acceleration algorithms used to speed up rendering and improve the quality of 3D design. By utilizing BSP technology, transparent objects can be drawn in any order in any scene, which solves the problem of incorrectly occluding transparent objects during rendering. This paper also applies collision detection technology, which enhances the user’s immersive feeling when roaming the landscape. In the 3D reconstruction process, it can complete the column and wall recognition for the test image with complex composition. Its recognition rate for various urban features has reached more than 80%.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/idt-230418","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
3D urban landscape visualization is a key technology in digital city construction. Based on the research and analysis of the three-dimensional space of the urban landscape space, the three-dimensional space can not only allow users to intuitively perceive the development of the city. It also enables decision makers, planners, and users to more intuitively, objectively, and rationally recognize and understand the current urban development and planning design. Defining the data content of the 3D city landscape image model is the basis for creating the 3D city image model. It not only guides producers to select data, but also serves as the basis for sharing data between different applications. With the continuous development of society, the number of people living in rural areas migrating to cities to make a living has increased rapidly, leading to the growing problem of “urban congestion” in many areas. In order to effectively solve these problems, “smart cities” came into being. It quickly triggered a boom in global urban development. Based on a survey of the state-of-the-art in the field of 3D modeling and engineering design visualization, this paper analyzes 3D rendering acceleration algorithms used to speed up rendering and improve the quality of 3D design. By utilizing BSP technology, transparent objects can be drawn in any order in any scene, which solves the problem of incorrectly occluding transparent objects during rendering. This paper also applies collision detection technology, which enhances the user’s immersive feeling when roaming the landscape. In the 3D reconstruction process, it can complete the column and wall recognition for the test image with complex composition. Its recognition rate for various urban features has reached more than 80%.