Layered point clouds: a simple and efficient multiresolution structure for distributing and rendering gigantic point-sampled models
- 作者：Gobbetti ; Enrico ; Marton ; Fabio
- 关键词：Point-based graphics ; Large datasets ; Out-of-core algorithms ; Level of detail
- 刊名：Computers and Graphics
- 出版年：2004
- 期刊代码：36_00978493
- 类别：cp
- 出版时间：December, 2004
- 卷：28
- 期：6
- 页码：815-826
- 文件大小：777 K
摘要
We recently introduced an efficient multiresolution structure for distributing and rendering very large point sampled models on consumer graphics platforms [1]. 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.