1. A Survey of Viewpoint Selection Methods for Polygonal Models
- Author
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Lewis L. Chuang, Christian Wallraven, Miquel Feixas, Mateu Sbert, Xavier Bonaventura, and Ministerio de Economía y Competitividad (Espanya)
- Subjects
Computer science ,General Physics and Astronomy ,lcsh:Astrophysics ,Visualització tridimensional (Informàtica) ,02 engineering and technology ,Article ,Computer graphics ,lcsh:QB460-466 ,0202 electrical engineering, electronic engineering, information engineering ,Entropy (information theory) ,viewpoint selection ,Camera placement ,lcsh:Science ,mutual information ,visualization ,Visualization ,Infografia tridimensional ,Information retrieval ,Entropia (Teoria de la informació) ,Cognitive neuroscience of visual object recognition ,Scientific visualization ,020207 software engineering ,Mutual information ,Visualització ,lcsh:QC1-999 ,entropy ,Entropy (Information theory) ,lcsh:Q ,020201 artificial intelligence & image processing ,Selection method ,Three-dimensional display systems ,Three-dimensional modeling ,lcsh:Physics - 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 This work has been partially funded by grant TIN2016-75866-C3-3-R from the Spanish Government, grant 2017-SGR-1101 from Catalan Government and by the National Natural Science Foundation of China (Nos. 61571439, 61471261 and 61771335)
- Published
- 2018