Parallel Rendering for Legible Illustrative Visualizations of Dense Geometries on Commodity CPUs

This paper presents a parallel visualization technique for illustrative rendering of dense three-dimensional (3D) geometry data sets. Our approach maps the depth information in each geometry onto various visual dimensions of graphical representations, including shape, color, brightness, transparency, and size, to achieve legible display in dense geometry environments where visual clutters often hinder perception and navigation in the visualizations. At the same time, we leverage legacy CPU computing power to overcome performance challenges as a result of the depth-dependent illustrations used for the visual legibility enhancement. This is realized by a novel parallel rendering algorithm we developed particularly for illustrative visualizations of depth-dependent stylized dense geometries at interactive frame rates. While the computation could be performed atop modern GPU devices, we target a parallel visualization framework that enables it to efficiently run on commodity CPUs, which are much more available than GPUs for ordinary users. We evaluated our framework with visualizations of depth-stylized geometries derived from 3D diffusion tensor MRI data, by comparing its efficiency with several other alternative parallelization platforms with respect to the same computations. Results show that our approach can efficiently render highly dense 3D geometry data sets and, thus, it offers not only an alternative and complementary, but also more adoptable, solution to users in contrast to parallel visualization environments that rely on GPUs.