机器学习视角下的城市建模计算机图形方法综述

冯天; 范非易; Tomasz BEDNARZ

doi:10.1631/FITEE.2000141

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机器学习视角下的城市建模计算机图形方法综述

常规文章 | Updated：2022-06-06

- 机器学习视角下的城市建模计算机图形方法综述
  Enhanced Publication
- A review of computer graphics approaches to urban modeling from a machine learning perspective
- 信息与电子工程前沿（英文） 2021年22卷第7期页码：915-925
- Affiliations：
  
  Department of Computer Science and Information Technology, La Trobe University, VIC 3086, Australia
  Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  Expanded Perception and Interaction Centre, University of New South Wales, NSW 2021, Australia
  CSIRO's Data61, NSW 2015, Australia
- Author bio：
  
  Tian FENG, E-mail: t.feng@latrobe.edu.au
- Funds：
- DOI：10.1631/FITEE.2000141
  中图分类号：
- 纸质出版日期：2021-07，
  
  网络出版日期：2021-05-22，
  
  收稿日期：2020-04-01，
  
  修回日期：2021-05-07，
  
  录用日期：2020-8-7
- Accepted：
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冯天, 范非易, Tomasz BEDNARZ. 机器学习视角下的城市建模计算机图形方法综述[J]. 信息与电子工程前沿（英文）, 2021,22(7):915-925.

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.
冯天, 范非易, Tomasz BEDNARZ. 机器学习视角下的城市建模计算机图形方法综述[J]. 信息与电子工程前沿（英文）, 2021,22(7):915-925. DOI： 10.1631/FITEE.2000141.

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. DOI： 10.1631/FITEE.2000141.

摘要

城市建模为生成城市不同场景下的虚拟环境提供了便利。城市建模需要专业知识和考虑，并消耗大量时间和计算资源。即便如此，与之相关的任务有时仍以不满意的结果甚至失败告终。这些挑战得到了计算机图形学领域学者的大量关注。同时，人工智能的蓬勃发展激励人们充分利用机器学习以改进现有解决方案。本文回顾了2010至2019年间发表的文献，对计算机图形领域中使用机器学习的城市建模方法进行综述。本文可作为机器学习视角下城市建模研究现状的概述。

Abstract

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.

关键词

城市建模计算机图形学机器学习深度学习

Keywords

Urban modelingComputer graphicsMachine learningDeep learning

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