Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Chagnon-Forget, M.; Rouhafzay, G.; Cretu, A. -M.; Bouchard, S.
Enhanced Visual-Attention Model for Perceptually Improved 3D Object Modeling in Virtual Environments Article de journal
Dans: 3D Research, vol. 7, no 4, 2016, ISSN: 20926731 (ISSN), (Publisher: 3D Display Research Center).
Résumé | Liens | BibTeX | Étiquettes: 3-d modeling, 3D modeling, Behavioral research, Complex networks, Interest points, Level of detail, Mesh simplification, Mesh simplifications, Saliency, Three dimensional computer graphics, virtual reality, Visual Attention
@article{chagnon-forget_enhanced_2016,
title = {Enhanced Visual-Attention Model for Perceptually Improved 3D Object Modeling in Virtual Environments},
author = {M. Chagnon-Forget and G. Rouhafzay and A. -M. Cretu and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991727567&doi=10.1007%2fs13319-016-0106-7&partnerID=40&md5=136feba48a1aa3ff798619f8d734c956},
doi = {10.1007/s13319-016-0106-7},
issn = {20926731 (ISSN)},
year = {2016},
date = {2016-01-01},
journal = {3D Research},
volume = {7},
number = {4},
abstract = {Three-dimensional object modeling and interactive virtual environment applications require accurate, but compact object models that ensure real-time rendering capabilities. In this context, the paper proposes a 3D modeling framework employing visual attention characteristics in order to obtain compact models that are more adapted to human visual capabilities. An enhanced computational visual attention model with additional saliency channels, such as curvature, symmetry, contrast and entropy, is initially employed to detect points of interest over the surface of a 3D object. The impact of the use of these supplementary channels is experimentally evaluated. The regions identified as salient by the visual attention model are preserved in a selectively-simplified model obtained using an adapted version of the QSlim algorithm. The resulting model is characterized by a higher density of points in the salient regions, therefore ensuring a higher perceived quality, while at the same time ensuring a less complex and more compact representation for the object. The quality of the resulting models is compared with the performance of other interest point detectors incorporated in a similar manner in the simplification algorithm. The proposed solution results overall in higher quality models, especially at lower resolutions. As an example of application, the selectively-densified models are included in a continuous multiple level of detail (LOD) modeling framework, in which an original neural-network solution selects the appropriate size and resolution of an object. © 2016, 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg.},
note = {Publisher: 3D Display Research Center},
keywords = {3-d modeling, 3D modeling, Behavioral research, Complex networks, Interest points, Level of detail, Mesh simplification, Mesh simplifications, Saliency, Three dimensional computer graphics, virtual reality, Visual Attention},
pubstate = {published},
tppubtype = {article}
}
Three-dimensional object modeling and interactive virtual environment applications require accurate, but compact object models that ensure real-time rendering capabilities. In this context, the paper proposes a 3D modeling framework employing visual attention characteristics in order to obtain compact models that are more adapted to human visual capabilities. An enhanced computational visual attention model with additional saliency channels, such as curvature, symmetry, contrast and entropy, is initially employed to detect points of interest over the surface of a 3D object. The impact of the use of these supplementary channels is experimentally evaluated. The regions identified as salient by the visual attention model are preserved in a selectively-simplified model obtained using an adapted version of the QSlim algorithm. The resulting model is characterized by a higher density of points in the salient regions, therefore ensuring a higher perceived quality, while at the same time ensuring a less complex and more compact representation for the object. The quality of the resulting models is compared with the performance of other interest point detectors incorporated in a similar manner in the simplification algorithm. The proposed solution results overall in higher quality models, especially at lower resolutions. As an example of application, the selectively-densified models are included in a continuous multiple level of detail (LOD) modeling framework, in which an original neural-network solution selects the appropriate size and resolution of an object. © 2016, 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg.