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Tian Feng


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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.7 P.915-925


A review of computer graphics approaches to urban modeling from a machine learning perspective

Author(s):  Tian Feng, Feiyi Fan, Tomasz Bednarz

Affiliation(s):  Department of Computer Science and Information Technology, La Trobe University, VIC 3086, Australia; more

Corresponding email(s):   t.feng@latrobe.edu.au

Key Words:  Urban modeling, Computer graphics, Machine learning, Deep learning

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Tian Feng, Feiyi Fan, Tomasz Bednarz. A review of computer graphics approaches to urban modeling from a machine learning perspective[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(7): 915-925.

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T1 - A review of computer graphics approaches to urban modeling from a machine learning perspective
A1 - Tian Feng
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DOI - 10.1631/FITEE.2000141

urban modeling facilitates the generation of virtual environments for various scenarios about cities. It requires expertise and consideration, and therefore consumes massive time and computation resources. Nevertheless, related tasks sometimes result in dissatisfaction or even failure. These challenges have received significant attention from researchers in the area of computer graphics. Meanwhile, the burgeoning development of artificial intelligence motivates people to exploit machine learning, and hence improves the conventional solutions. In this paper, we present a review of approaches to urban modeling in computer graphics using machine learning in the literature published between 2010 and 2019. This serves as an overview of the current state of research on urban modeling from a machine learning perspective.


冯天1,范非易2,Tomasz BEDNARZ3,4


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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