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多边形模型视点选择方法综述

A Survey of Viewpoint Selection Methods for Polygonal Models.

作者信息

Bonaventura Xavier, Feixas Miquel, Sbert Mateu, Chuang Lewis, Wallraven Christian

机构信息

Graphics & Imaging Laboratory, University of Girona, Girona 17003, Spain.

School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.

出版信息

Entropy (Basel). 2018 May 16;20(5):370. doi: 10.3390/e20050370.

Abstract

Viewpoint selection has been an emerging area in computer graphics for some years, and it is now getting maturity with applications in fields such as scene navigation, scientific visualization, object recognition, mesh simplification, and camera placement. In this survey, we review and compare twenty-two measures to select good views of a polygonal 3D model, classify them using an extension of the categories defined by Secord et al., and evaluate them against the Dutagaci et al. benchmark. Eleven of these measures have not been reviewed in previous surveys. Three out of the five short-listed best viewpoint measures are directly related to information. We also present in which fields the different viewpoint measures have been applied. Finally, we provide a publicly available framework where all the viewpoint selection measures are implemented and can be compared against each other.

摘要

多年来,视点选择一直是计算机图形学中一个新兴的领域,现在随着其在场景导航、科学可视化、目标识别、网格简化和相机放置等领域的应用而逐渐走向成熟。在本次综述中,我们回顾并比较了二十二种用于选择多边形三维模型良好视图的方法,使用Secord等人定义的类别扩展对它们进行分类,并根据Dutagaci等人的基准对它们进行评估。这些方法中有十一种在以前的综述中未曾被回顾过。入围的五个最佳视点方法中有三个与信息直接相关。我们还介绍了不同视点方法已应用于哪些领域。最后,我们提供了一个公开可用的框架,其中实现了所有视点选择方法并且可以相互比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/201c/7512891/8249fd872b16/entropy-20-00370-g001.jpg

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