Screen-Space Normal Distribution Function Caching for Consistent Multi-Resolution Rendering of Large Particle Data
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Visual Computing Center (VCC)
Online Publication Date2017-08-28
Print Publication Date2018-01
Permanent link to this recordhttp://hdl.handle.net/10754/625429
MetadataShow full item record
AbstractMolecular dynamics (MD) simulations are crucial to investigating important processes in physics and thermodynamics. The simulated atoms are usually visualized as hard spheres with Phong shading, where individual particles and their local density can be perceived well in close-up views. However, for large-scale simulations with 10 million particles or more, the visualization of large fields-of-view usually suffers from strong aliasing artifacts, because the mismatch between data size and output resolution leads to severe under-sampling of the geometry. Excessive super-sampling can alleviate this problem, but is prohibitively expensive. This paper presents a novel visualization method for large-scale particle data that addresses aliasing while enabling interactive high-quality rendering. We introduce the novel concept of screen-space normal distribution functions (S-NDFs) for particle data. S-NDFs represent the distribution of surface normals that map to a given pixel in screen space, which enables high-quality re-lighting without re-rendering particles. In order to facilitate interactive zooming, we cache S-NDFs in a screen-space mipmap (S-MIP). Together, these two concepts enable interactive, scale-consistent re-lighting and shading changes, as well as zooming, without having to re-sample the particle data. We show how our method facilitates the interactive exploration of real-world large-scale MD simulation data in different scenarios.
CitationIbrahim M, Wickenhauser P, Rautek P, Reina G, Hadwiger M (2017) Screen-Space Normal Distribution Function Caching for Consistent Multi-Resolution Rendering of Large Particle Data. IEEE Transactions on Visualization and Computer Graphics: 1–1. Available: http://dx.doi.org/10.1109/TVCG.2017.2743979.
SponsorsThis work was funded by King Abdullah University of Science and Technology (KAUST), and Deutsche Forschungsgemeinschaft (DFG) as part of SFB 716.