Layered Point Clouds - a Simple and Efficient Multiresolution Structure for Distributing and Rendering Gigantic Point-Sampled Models

Enrico Gobbetti; Fabio Marton

Layered Point Clouds - a Simple and Efficient Multiresolution Structure for Distributing and Rendering Gigantic Point-Sampled Models

Enrico Gobbetti and Fabio Marton
December 2004

Abstract

We recently introduced an efficient multiresolution structure for distributing and rendering very large point sampled models on consumer graphics platforms CITEGobbetti:2004:LPC. The structure is based on a hierarchy of precomputed object-space point clouds, that are combined coarse-to-fine at rendering time to locally adapt sample densities according to the projected size in the image. The progressive block based refinement nature of the rendering traversal exploits on-board caching and object based rendering APIs, hides out-of-core data access latency through speculative prefetching, and lends itself well to incorporate backface, view frustum, and occlusion culling, as well as compression and view-dependent progressive transmission. The resulting system allows rendering of complex out-of-core models at high frame rates (over 60M rendered points/second), supports network streaming, and is fundamentally simple to implement. We demonstrate the efficiency of the approach on a number of very large models, stored on local disks or accessed through a consumer level broadband network, including a massive 234M samples isosurface generated by a compressible turbulence simulation and a 167M samples model of Michelangelo's St. Matthew. Many of the details of our framework were presented in a previous study. We here provide a more thorough exposition, but also significant new material, including the presentation of a higher quality bottom-up construction method and additional qualitative and quantitative results.

Reference and download information

Enrico Gobbetti and Fabio Marton. Layered Point Clouds - a Simple and Efficient Multiresolution Structure for Distributing and Rendering Gigantic Point-Sampled Models. Computers & Graphics, 28(6): 815-826, December 2004.

Related multimedia productions

	Enrico Gobbetti and Fabio Marton Layered Point Clouds CRS4 Video n. 120 - Date: 04/2004 Presented at the First Eurographics Symposium on Point-Based Graphics, Zurich, Switzerland, June 2-4, 2004.
	Paolo Cignoni, Fabio Ganovelli, Enrico Gobbetti, Fabio Marton, Federico Ponchio, and Roberto Scopigno Efficient Visualization of Large Scale Models on Commodity Graphics Platforms CRS4 Video n. 118 - Date: 01/2004 - Duration: 00:07:50

Bibtex citation record

@Article{Gobbetti:2004:lpcb, author = {Enrico Gobbetti and Fabio Marton}, title = {Layered Point Clouds -- a Simple and Efficient Multiresolution Structure for Distributing and Rendering Gigantic Point-Sampled Models}, journal = {Computers \& Graphics}, volume = {28}, number = {6}, pages = {815--826}, publisher = {Elsevier Science Publishers B. V.}, address = {Amsterdam, The Netherlands}, month = {December}, year = {2004}, abstract = { We recently introduced an efficient multiresolution structure for distributing and rendering very large point sampled models on consumer graphics platforms~\cite{Gobbetti:2004:LPC}. The structure is based on a hierarchy of precomputed object-space point clouds, that are combined coarse-to-fine at rendering time to locally adapt sample densities according to the projected size in the image. The progressive block based refinement nature of the rendering traversal exploits on-board caching and object based rendering APIs, hides out-of-core data access latency through speculative prefetching, and lends itself well to incorporate backface, view frustum, and occlusion culling, as well as compression and view-dependent progressive transmission. The resulting system allows rendering of complex out-of-core models at high frame rates (over 60M rendered points/second), supports network streaming, and is fundamentally simple to implement. We demonstrate the efficiency of the approach on a number of very large models, stored on local disks or accessed through a consumer level broadband network, including a massive 234M samples isosurface generated by a compressible turbulence simulation and a 167M samples model of Michelangelo's St. Matthew. Many of the details of our framework were presented in a previous study. We here provide a more thorough exposition, but also significant new material, including the presentation of a higher quality bottom-up construction method and additional qualitative and quantitative results. }, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Gobbetti:2004:lpcb'}, }