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Article
Günther, F., Jiang, C., and Pottmann, H. 2020. Smooth polyhedral surfaces. Advances in Mathematics363.
(arXiv: 1703.05318)
Abstract
Polyhedral surfaces are fundamental objects in architectural geometry and industrial design. Whereas closeness of a given mesh to a smooth reference surface and its suitability for numerical simulations were already studied extensively, the aim of our work is to find and to discuss suitable assessments of smoothness of polyhedral surfaces that only take the geometry of the polyhedral surface itself into account. Motivated by analogies to classical differential geometry, we propose a theory of smoothness of polyhedral surfaces including suitable notions of normal vectors, tangent planes, asymptotic directions, and parabolic curves that are invariant under projective transformations. It is remarkable that seemingly mild conditions significantly limit the shapes of faces of a smooth polyhedral surface. Besides being of theoretical interest, we believe that smoothness of polyhedral surfaces is of interest in the architectural context, where vertices and edges of polyhedral surfaces are highly visible.
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BibTeX
@article{Guenther2020, TITLE = {Smooth polyhedral surfaces}, AUTHOR = {G{\"u}nther, Felix and Jiang, Caigui and Pottmann, Helmut}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1703.05318}, DOI = {10.1016/j.aim.2020.107004}, EPRINT = {1703.05318}, EPRINTTYPE = {arXiv}, PUBLISHER = {Elsevier}, YEAR = {2020}, DATE = {2020}, ABSTRACT = {Polyhedral surfaces are fundamental objects in architectural geometry and industrial design. Whereas closeness of a given mesh to a smooth reference surface and its suitability for numerical simulations were already studied extensively, the aim of our work is to find and to discuss suitable assessments of smoothness of polyhedral surfaces that only take the geometry of the polyhedral surface itself into account. Motivated by analogies to classical differential geometry, we propose a theory of smoothness of polyhedral surfaces including suitable notions of normal vectors, tangent planes, asymptotic directions, and parabolic curves that are invariant under projective transformations. It is remarkable that seemingly mild conditions significantly limit the shapes of faces of a smooth polyhedral surface. Besides being of theoretical interest, we believe that smoothness of polyhedral surfaces is of interest in the architectural context, where vertices and edges of polyhedral surfaces are highly visible.}, JOURNAL = {Advances in Mathematics}, VOLUME = {363}, EID = {107004}, }
Endnote
%0 Journal Article %A Günther, Felix %A Jiang, Caigui %A Pottmann, Helmut %+ External Organizations Computer Graphics, MPI for Informatics, Max Planck Society External Organizations %T Smooth polyhedral surfaces : %G eng %U http://hdl.handle.net/21.11116/0000-0006-9760-3 %U http://arxiv.org/abs/1703.05318 %R 10.1016/j.aim.2020.107004 %D 2020 %* Review method: peer-reviewed %X Polyhedral surfaces are fundamental objects in architectural geometry and industrial design. Whereas closeness of a given mesh to a smooth reference surface and its suitability for numerical simulations were already studied extensively, the aim of our work is to find and to discuss suitable assessments of smoothness of polyhedral surfaces that only take the geometry of the polyhedral surface itself into account. Motivated by analogies to classical differential geometry, we propose a theory of smoothness of polyhedral surfaces including suitable notions of normal vectors, tangent planes, asymptotic directions, and parabolic curves that are invariant under projective transformations. It is remarkable that seemingly mild conditions significantly limit the shapes of faces of a smooth polyhedral surface. Besides being of theoretical interest, we believe that smoothness of polyhedral surfaces is of interest in the architectural context, where vertices and edges of polyhedral surfaces are highly visible. %K Mathematics, Metric Geometry, Mathematics, Differential Geometry %J Advances in Mathematics %O Adv. Math. %V 363 %Z sequence number: 107004 %I Elsevier
Singh, G., Subr, K., Coeurjolly, D., Ostromoukhov, V., and Jarosz, W. 2020. Fourier Analysis of Correlated Monte Carlo Importance Sampling. Computer Graphics Forum39, 1.
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@article{SinghCGF2020, TITLE = {Fourier Analysis of Correlated {Monte Carlo} Importance Sampling}, AUTHOR = {Singh, Gurprit and Subr, Kartic and Coeurjolly, David and Ostromoukhov, Victor and Jarosz, Wojciech}, LANGUAGE = {eng}, ISSN = {0167-7055}, DOI = {10.1111/cgf.13613}, PUBLISHER = {Blackwell-Wiley}, ADDRESS = {Oxford}, YEAR = {2020}, DATE = {2020}, JOURNAL = {Computer Graphics Forum}, VOLUME = {39}, NUMBER = {1}, PAGES = {7--19}, }
Endnote
%0 Journal Article %A Singh, Gurprit %A Subr, Kartic %A Coeurjolly, David %A Ostromoukhov, Victor %A Jarosz, Wojciech %+ Computer Graphics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T Fourier Analysis of Correlated Monte Carlo Importance Sampling : %G eng %U http://hdl.handle.net/21.11116/0000-0006-978D-1 %R 10.1111/cgf.13613 %7 2020 %D 2020 %J Computer Graphics Forum %O Computer Graphics Forum : journal of the European Association for Computer Graphics Comput. Graph. Forum %V 39 %N 1 %& 7 %P 7 - 19 %I Blackwell-Wiley %C Oxford %@ false
Sultan, A.S., Elgharib, M., Tavares, T., Jessri, M., and Basile, J.R. 2020. The Use of Artificial Intelligence, Machine Learning and Deep Learning in Oncologic Histopathology. Journal of Oral Pathology & Medicine.
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@article{Sultan2020, TITLE = {The Use of Artificial Intelligence, Machine Learning and Deep Learning in Oncologic Histopathology}, AUTHOR = {Sultan, Ahmed S. and Elgharib, Mohamed and Tavares, Tiffany and Jessri, Maryam and Basile, John R.}, LANGUAGE = {eng}, ISSN = {0904-2512}, DOI = {10.1111/jop.13042}, PUBLISHER = {Wiley-Blackwell}, ADDRESS = {Oxford}, YEAR = {2020}, JOURNAL = {Journal of Oral Pathology \& Medicine}, }
Endnote
%0 Journal Article %A Sultan, Ahmed S. %A Elgharib, Mohamed %A Tavares, Tiffany %A Jessri, Maryam %A Basile, John R. %+ External Organizations Computer Graphics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T The Use of Artificial Intelligence, Machine Learning and Deep Learning in Oncologic Histopathology : %G eng %U http://hdl.handle.net/21.11116/0000-0006-A2C9-0 %R 10.1111/jop.13042 %7 2020 %D 2020 %J Journal of Oral Pathology & Medicine %I Wiley-Blackwell %C Oxford %@ false
Conference Paper
Huang, L., Gao, C., Zhou, Y., et al. Universal Physical Camouflage Attacks on Object Detectors. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), IEEE.
(Accepted/in press)
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@inproceedings{Huang_CVPR2020, TITLE = {Universal Physical Camou{fl}age Attacks on Object Detectors}, AUTHOR = {Huang, Lifeng and Gao, Chengying and Zhou, Yuyin and Xie, Cihang and Yuille, Alan and Zou, Changqing and Liu, Ning}, LANGUAGE = {eng}, PUBLISHER = {IEEE}, YEAR = {2020}, PUBLREMARK = {Accepted}, BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)}, ADDRESS = {Seattle, WA, USA}, }
Endnote
%0 Conference Proceedings %A Huang, Lifeng %A Gao, Chengying %A Zhou, Yuyin %A Xie, Cihang %A Yuille, Alan %A Zou, Changqing %A Liu, Ning %+ External Organizations External Organizations External Organizations External Organizations External Organizations Computer Graphics, MPI for Informatics, Max Planck Society External Organizations %T Universal Physical Camouflage Attacks on Object Detectors : %G eng %U http://hdl.handle.net/21.11116/0000-0006-09F0-1 %D 2020 %B 33rd IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2020-06-14 - 2020-06-19 %C Seattle, WA, USA %B IEEE/CVF Conference on Computer Vision and Pattern Recognition %I IEEE
Report
Qian, N., Wang, J., Mueller, F., Bernard, F., Golyanik, V., and Theobalt, C. 2020. Parametric Hand Texture Model for 3D Hand Reconstruction and Personalization. Max-Planck-Institut für Informatik, Saarbrücken.
Abstract
3D hand reconstruction from image data is a widely-studied problem in com- puter vision and graphics, and has a particularly high relevance for virtual and augmented reality. Although several 3D hand reconstruction approaches leverage hand models as a strong prior to resolve ambiguities and achieve a more robust reconstruction, most existing models account only for the hand shape and poses and do not model the texture. To fill this gap, in this work we present the first parametric texture model of human hands. Our model spans several dimensions of hand appearance variability (e.g., related to gen- der, ethnicity, or age) and only requires a commodity camera for data acqui- sition. Experimentally, we demonstrate that our appearance model can be used to tackle a range of challenging problems such as 3D hand reconstruc- tion from a single monocular image. Furthermore, our appearance model can be used to define a neural rendering layer that enables training with a self-supervised photometric loss. We make our model publicly available.
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@techreport{Qian_report2020, TITLE = {Parametric Hand Texture Model for {3D} Hand Reconstruction and Personalization}, AUTHOR = {Qian, Neng and Wang, Jiayi and Mueller, Franziska and Bernard, Florian and Golyanik, Vladislav and Theobalt, Christian}, LANGUAGE = {eng}, ISSN = {0946-011X}, NUMBER = {MPI-I-2020-4-001}, INSTITUTION = {Max-Planck-Institut f{\"u}r Informatik}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2020}, ABSTRACT = {3D hand reconstruction from image data is a widely-studied problem in com- puter vision and graphics, and has a particularly high relevance for virtual and augmented reality. Although several 3D hand reconstruction approaches leverage hand models as a strong prior to resolve ambiguities and achieve a more robust reconstruction, most existing models account only for the hand shape and poses and do not model the texture. To {fi}ll this gap, in this work we present the {fi}rst parametric texture model of human hands. Our model spans several dimensions of hand appearance variability (e.g., related to gen- der, ethnicity, or age) and only requires a commodity camera for data acqui- sition. Experimentally, we demonstrate that our appearance model can be used to tackle a range of challenging problems such as 3D hand reconstruc- tion from a single monocular image. Furthermore, our appearance model can be used to de{fi}ne a neural rendering layer that enables training with a self-supervised photometric loss. We make our model publicly available.}, TYPE = {Research Report}, }
Endnote
%0 Report %A Qian, Neng %A Wang, Jiayi %A Mueller, Franziska %A Bernard, Florian %A Golyanik, Vladislav %A Theobalt, Christian %+ Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society %T Parametric Hand Texture Model for 3D Hand Reconstruction and Personalization : %G eng %U http://hdl.handle.net/21.11116/0000-0006-9128-9 %Y Max-Planck-Institut für Informatik %C Saarbrücken %D 2020 %P 37 p. %X 3D hand reconstruction from image data is a widely-studied problem in com- puter vision and graphics, and has a particularly high relevance for virtual and augmented reality. Although several 3D hand reconstruction approaches leverage hand models as a strong prior to resolve ambiguities and achieve a more robust reconstruction, most existing models account only for the hand shape and poses and do not model the texture. To fill this gap, in this work we present the first parametric texture model of human hands. Our model spans several dimensions of hand appearance variability (e.g., related to gen- der, ethnicity, or age) and only requires a commodity camera for data acqui- sition. Experimentally, we demonstrate that our appearance model can be used to tackle a range of challenging problems such as 3D hand reconstruc- tion from a single monocular image. Furthermore, our appearance model can be used to define a neural rendering layer that enables training with a self-supervised photometric loss. We make our model publicly available. %K hand texture model, appearance modeling, hand tracking, 3D hand recon- struction %B Research Report %@ false