Compression and Occlusion Culling for Fast Isosurface Extraction from Massive Datasets

2009 ◽  
pp. 303-323 ◽  
Author(s):  
Benjamin Gregorski ◽  
Joshua Senecal ◽  
Mark Duchaineau ◽  
Kenneth I. Joy
Keyword(s):  
Massive Datasets ◽  
Isosurface Extraction ◽  
Occlusion Culling

Mining of Massive Datasets

2020 ◽  
Author(s):  
Jure Leskovec ◽  
Anand Rajaraman ◽  
Jeffrey David Ullman
Keyword(s):  
Massive Datasets

scholarly journals Occlusion Culling for Wide-Angle Computer-Generated Holograms Using Phase Added Stereogram Technique

Photonics ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 298
Author(s):  
Juan Martinez-Carranza ◽  
Tomasz Kozacki ◽  
Rafał Kukołowicz ◽  
Maksymilian Chlipala ◽  
Moncy Sajeev Idicula
Keyword(s):  
Point Sources ◽  
Wide Angle ◽  
Computer Generated Holograms ◽  
Propagation Methods ◽  
Propagation Technique ◽  
3D Scene ◽  
Occlusion Culling ◽  
Fourier Type ◽  
Cloud Of Points

A computer-generated hologram (CGH) allows synthetizing view of 3D scene of real or virtual objects. Additionally, CGH with wide-angle view offers the possibility of having a 3D experience for large objects. An important feature to consider in the calculation of CGHs is occlusion between surfaces because it provides correct perception of encoded 3D scenes. Although there is a vast family of occlusion culling algorithms, none of these, at the best of our knowledge, consider occlusion when calculating CGHs with wide-angle view. For that reason, in this work we propose an occlusion culling algorithm for wide-angle CGHs that uses the Fourier-type phase added stereogram (PAS). It is shown that segmentation properties of the PAS can be used for setting efficient conditions for occlusion culling of hidden areas. The method is efficient because it enables processing of dense cloud of points. The investigated case has 24 million of point sources. Moreover, quality of the occluded wide-angle CGHs is tested by two propagation methods. The first propagation technique quantifies quality of point reproduction of calculated CGH, while the second method enables the quality assessment of the occlusion culling operation over an object of complex shape. Finally, the applicability of proposed occlusion PAS algorithm is tested by synthetizing wide-angle CGHs that are numerically and optically reconstructed.

scholarly journals Stochastic Local Interaction Model: An Alternative to Kriging for Massive Datasets

2021 ◽  
Author(s):  
Dionissios T. Hristopulos ◽  
Andrew Pavlides ◽  
Vasiliki D. Agou ◽  
Panagiota Gkafa
Keyword(s):  
Interaction Model ◽  
Local Interaction ◽  
Massive Datasets

scholarly journals Sparse HJ Biplot: A New Methodology via Elastic Net

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1298
Author(s):  
Mitzi Cubilla-Montilla ◽  
Ana Belén Nieto-Librero ◽  
M. Purificación Galindo-Villardón ◽  
Carlos A. Torres-Cubilla
Keyword(s):  
Multivariate Analysis ◽  
Elastic Net ◽  
Massive Datasets ◽  
New Techniques ◽  
Modern Approach ◽  
R Language ◽  
Reduced Dimensions ◽  
Analysis Technique ◽  
Practical Support ◽  
Novel Algorithm

The HJ biplot is a multivariate analysis technique that allows us to represent both individuals and variables in a space of reduced dimensions. To adapt this approach to massive datasets, it is necessary to implement new techniques that are capable of reducing the dimensionality of the data and improving interpretation. Because of this, we propose a modern approach to obtaining the HJ biplot called the elastic net HJ biplot, which applies the elastic net penalty to improve the interpretation of the results. It is a novel algorithm in the sense that it is the first attempt within the biplot family in which regularisation methods are used to obtain modified loadings to optimise the results. As a complement to the proposed method, and to give practical support to it, a package has been developed in the R language called SparseBiplots. This package fills a gap that exists in the context of the HJ biplot through penalized techniques since in addition to the elastic net, it also includes the ridge and lasso to obtain the HJ biplot. To complete the study, a practical comparison is made with the standard HJ biplot and the disjoint biplot, and some results common to these methods are analysed.

Introduction to the Special Section on Massive Datasets

1999 ◽  
Vol 8 (3) ◽  
pp. 544-544 ◽  
Author(s):  
Andreas Buja ◽  
Sallie Keller-McNulty
Keyword(s):  
Special Section ◽  
Massive Datasets
Sign in / Sign up

Export Citation Format

Share Document