Hardware-accelerated Stippling of Surfaces derived from Medical Volume Data

Abstract

We present a fast hardware-accelerated stippling method which does not require any preprocessing for placing points on surfaces. The surfaces are automatically parameterized in order to apply stippling textures without major distortions. The mapping process is guided by a decomposition of the space in cubes. Seamless scaling with a constant density of points is realized by subdividing and summarizing cubes. Our mip-map technique enables arbitrarily scaling with one texture. Different shading tones and scales are facilitated by adhering to the constraints of tonal art maps. With our stippling technique, it is feasible to encode all scaling and brightness levels within one self-similar texture. Our method is applied to surfaces extracted from (segmented) medical volume data. The speed of the stippling process enables stippling for several complex objects simultaneously. We consider application scenarios in intervention planning (neck and liver surgery planning). In these scenarios, object recognition (shape perception) is supported by adding stippling to semi-transparently shaded objects which are displayed as context information.