RBF Volume Ray Casting on Multicore and Manycore CPUs

• Published in: Computer graphics forum Vol. 33; no. 3; pp. 71 - 80
• Main Authors: Knoll, Aaron, Wald, Ingo, Navratil, Paul, Bowen, Anne, Reda, Khairi, Papka, Michael E., Gaither, Kelly
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.06.2014
• Subjects: Algorithms; Analysis; Categories and Subject Descriptors (according to ACM CCS); Central processing units; Classification; Computer graphics; I.3.2 [Computer Graphics]: Graphics Systems-Distributed/network graphics; I.3.3 [Computer Graphics]: Picture/Image Generation-Line and curve generation; I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism-Raytracing; Kernels; Molecular dynamics; Reconstruction; Rendering; Sampling; Studies; Supercomputers
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.12363

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.