Representational image generation for 3D objects

Serin, Ekrem; Sumengen, Selcuk; Balcısoy, Selim

doi:10.1007/s00371-013-0805-5

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…Although some of them introduced information theory (such as Shannon Entropy) to quantify information captured from 3D model [3,4,11,12], lacking structural information makes it hard to truly reflect the perceptual habits of human beings [15]. As the 5th type evaluates viewpoints through high-level even semantic information extracted from 3D object, it avoids the problem of other four types [15,16].…”

Section: A View Selection Methodsmentioning

confidence: 99%

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View Selection of 3D Objects Based on Saliency Segmentation

Han

Wang

et al. 2014

2014 International Conference on Virtual Reality and Visualization

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Getting good viewpoints has been considered important for promoting the efficiency when investigating a model. Many view selection methods therefore have been proposed. In particular, measuring semantic meaning of the model features through segmentation is regarded more effective to get optimal viewpoints. Unfortunately, the semantic meanings of the model are usually obtained through experiences, which are not easy to apply into practices. Given these considerations, a new method is proposed for view selection via saliency segmentation in this paper. Due to the fact that mesh saliency computation can recognize the features of a model in a way that similar to human perception and also a novel viewpoint ranking equation has been developed, our method is more reasonable than existing methods. The results of experiments show the effectiveness of our method, and it is consistent with the results of a user study we conducted.

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Section: A View Selection Methodsmentioning

confidence: 99%

“…They classified these methods into five types according to the attributes of views used in measuring view goodness: (1) area, e.g. projected area, surface visibility [2] and viewpoint entropy [3,4]; (2) contour [5,6]; (3) depth [7,8]; (4) surface curvature [9][10][11][12][13]; (5) semantic [7,[14][15][16].…”

Section: A View Selection Methodsmentioning

confidence: 99%

View Selection of 3D Objects Based on Saliency Segmentation

Han

Wang

et al. 2014

2014 International Conference on Virtual Reality and Visualization

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“…Leifman et al [25] proposed a viewpoint selection method based on the interest regions of mesh surfaces. Serin et al [26] researched representational images of 3D objects; this approach has also been applied to select optimal viewpoints. Han et al [27] studied a 3D object viewpoint selection method based on saliency segmentation.…”

Section: A 3d Viewpoint Research Basismentioning

confidence: 99%

3D Viewpoint Estimation Based on Aesthetics

2020

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Research on viewpoint in 3D scenes has been a cutting-edge subject in computer vision and 3D scene understanding. Calculating a high quality 3D viewpoint in virtual scenes is conducive to a more comprehensive analysis of 3D graphic structure, which can contribute to an overall understanding of virtual environments. 3D viewpoint research can also be applied to the analysis of structural relationships in virtual scenes and to exploit hidden hierarchical structures, thus it plays a role in fields such as scientific visualization computing or image-based scene modeling. Viewpoint research combined with relevant research results of human visual perception or visual psychology can be used to analyze the visual interest and focus areas of 3D objects in human vision, thus effectively improving the quality of mesh simplification and rendering efficiency, as well as providing an optimization basis for highly complex scenes. In fields such as 3D computer games, virtual reality, and landscape animation, research on high-quality viewpoints can be adopted to optimize global lighting and illumination. Additionally, practical applications can be realized through user attention analysis and aesthetic improvement strategies based on high-quality viewpoints, such as intelligent modeling and scene optimization calculations. In common fields such as 3D model retrieval, practical applications can be realized for saliency research, scientific visualization computing, and medical 3D imaging. In-depth research has been performed and extensive applications have been developed that make use of viewpoint analysis. In this paper, we introduce an optimization strategy to calculate high-quality virtual viewpoints for aesthetic images. A novel framework is proposed for modifying and optimizing the viewpoint calculation model by combining a multibranch CNN and a viewpoint correction method, thus realizing rendered images with a higher aesthetic quality. By attempting to fully integrate the visual perception with the geometric information calculation, we expect this method to achieve more comprehensive applications in many practice areas in the future, such as the realization of aesthetically based virtual camera path planning and analysis of the aesthetic characteristics of virtual environments.

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“…Saliency-based EVMI is sensitive to polygonal discretization because the saliency of a polygon is sensitive too. Serin et al [ 53 ] presented a similar measure where

is given by the surface curvature and

(i.e., average projected area) is substituted by the total area of the polygon.…”

Section: Viewpoint Selection Measuresmentioning

confidence: 99%

A Survey of Viewpoint Selection Methods for Polygonal Models

Bonaventura

Feixas

Sbert

et al. 2018

Entropy

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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.

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Representational image generation for 3D objects

Cited by 8 publications

References 14 publications

View Selection of 3D Objects Based on Saliency Segmentation

View Selection of 3D Objects Based on Saliency Segmentation

3D Viewpoint Estimation Based on Aesthetics

A Survey of Viewpoint Selection Methods for Polygonal Models

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