RBF Volume Ray Casting on Multicore and Manycore CPUs

作     者：Knoll, Aaron Wald, Ingo Navratil, Paul Bowen, Anne Reda, Khairi Papka, Michael E. Gaither, Kelly 

作者机构：Univ Texas Austin Texas Adv Comp Ctr Austin TX 78712 USA Intel Corp Santa Clara CA 95051 USA Univ Illinois Elect Visualizat Lab Chicago IL USA Argonne Natl Lab Argonne IL 60439 USA 

出 版 物：《COMPUTER GRAPHICS FORUM》 (计算机图形学论坛)

年 卷 期：2014年第33卷第3期

页      面：71-80页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science Foundation [OCI-1134872, ACI-1339863] Office of Science of the U.S. Department of Energy [DE-AC02-06CH11357] Office of Advanced Cyberinfrastructure (OAC) Direct For Computer & Info Scie & Enginr Funding Source: National Science Foundation 

主　　题：Categories and Subject Descriptors (according to ACM CCS) I.3.3 [Computer Graphics]: Picture/Image Generation&mdash Line and curve generation I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism&mdash Raytracing I.3.2 [Computer Graphics]: Graphics Systems&mdash Distributed/network graphics 

摘      要：Modern supercomputers enable increasingly large N-body simulations using unstructured point data. The structures implied by these points can be reconstructed implicitly. Direct volume rendering of radial basis function (RBF) kernels in domain-space offers flexible classification and robust feature reconstruction, but achieving performant RBF volume rendering remains a challenge for existing methods on both CPUs and accelerators. In this paper, we present a fast CPU method for direct volume rendering of particle data with RBF kernels. We propose a novel two-pass algorithm: first sampling the RBF field using coherent bounding hierarchy traversal, then subsequently integrating samples along ray segments. Our approach performs interactively for a range of data sets from molecular dynamics and astrophysics up to 82 million particles. It does not rely on level of detail or subsampling, and offers better reconstruction quality than structured volume rendering of the same data, exhibiting comparable performance and requiring no additional preprocessing or memory footprint other than the BVH. Lastly, our technique enables multi-field, multi-material classification of particle data, providing better insight and analysis.