地图学与地理信息

小规模地理场景中点要素三维注记优化配置算法

  • 周鑫鑫 ,
  • 吴长彬 ,
  • 孙在宏 ,
  • 丁远 ,
  • 贺涛
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  • 南京师范大学虚拟地理环境教育部重点实验室, 江苏 南京 210046
周鑫鑫(1991-),男,硕士,研究方向为三维地籍及地图制图。E-mail:windofmay@foxmail.com

收稿日期: 2016-05-03

  修回日期: 2016-10-11

  网络出版日期: 2017-01-02

基金资助

国家自然科学基金(41471318)

A 3D Annotation Optimal Placement Algorithm for the Point Features in the Small Scale Geographic Scene

  • ZHOU Xinxin ,
  • WU Changbin ,
  • SUN Zaihong ,
  • DING Yuan ,
  • HE Tao
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  • Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China

Received date: 2016-05-03

  Revised date: 2016-10-11

  Online published: 2017-01-02

Supported by

The National Natural Science Foundation of China (No.41471318)

摘要

地理场景中点要素三维注记配置规则多为“遮挡则不显示”和“遮挡直接显示”,该类规则的缺陷是注记信息丢失或存在大量遮挡,普适性不强,尤其不适用于小规模地理场景点要素三维注记配置。本文首先归纳了三维注记绘制的内容、位置及方法,并针对小规模地理场景点要素三维注记配置问题,以“信息不丢失、注记尽可能少的遮挡”为配置目标,配置规则为“遮挡后优化并显示”。算法以透视变换矩阵、逆透视变换矩阵及GRID算法为基础,以遗传算法为核心,以三维注记质量评价函数为遗传算法适应度评价函数,实现点要素三维注记的可行最优解求解。经多视角、多平台对照试验可知,本算法适用于多视角三维注记优化配置,具备普适性;与主流GIS平台(SuperMap Desktop、ArcScene)的三维注记配置效果作对比,本算法三维注记质量值分别相对提升144%、232%,符合配置目标。

本文引用格式

周鑫鑫 , 吴长彬 , 孙在宏 , 丁远 , 贺涛 . 小规模地理场景中点要素三维注记优化配置算法[J]. 测绘学报, 2016 , 45(12) : 1476 -1484 . DOI: 10.11947/j.AGCS.2016.20160210

Abstract

The 3D annotations placement rules of point features in geographic scene are "obscured then not showing" and "obscured then directly showing" normally. The defects of those rules are annotation information lost or large numbers of occlusion, so their universalities are not strong and they are not suitable for the annotation placement of small-scaled geographic scene. This paper summarizes the contents, position and placement methods of 3D annotation and takes the aim at "not loss of annotation information and less annotation obscured as far as possible" of the research problem of the annotation placement of small-scaled geographic scene. The configuration rule of 3D annotation Identifies as "obscured then optimized to display". The designed algorithm based on the perspective transformation matrix, the inverse perspective transformation matrix and the grid algorithm takes the genetic algorithm (GA) whose fitness evaluation function uses the 3D Annotation quality evaluation function as the core to realize the feasible optimal solution of 3D annotations of point features in geographic scene. By the multi-views, multi-platforms contrast experiment, this algorithm is applicable for multi-views 3D annotation placement widely. The 3D annotation effect is better than mainstream GIS platforms (such as SuperMap desktop, ArcScene), which assumes that the algorithm's 3D annotation quality value is relatively increased 144%, 232%. The algorithm fits in with the target configuration.

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