Using motion to illustrate static 3D shape - kinetic visualization

Abstract-The perception of transparency and the underlying neural mechanisms have been subject to extensive research in the cognitive sciences. However, we have yet to develop visualization techniques which optimally convey the inner structure of complex transparent shapes. In this paper we apply the findings of perception research to develop a novel illustrative rendering method that optimizes surface transparency globally. Rendering of transparent geometry is computationally expensive since many optimizations, such as visibility culling, are not applicable and fragments have to be sorted by depth for correct blending. In order to overcome these difficulties efficiently we propose the illustration buffer. This novel data structure combines the ideas of the A-and G-buffers to store a list of all surface layers for each pixel. A set of local and non-local operators is then used to process these depth-lists to generate the final image. Our technique is interactive on current graphics hardware and is only limited by the available graphics memory. Based on this framework we present an efficient algorithm for non-local transparency optimization which creates expressive renderings of transparent surfaces. A controlled quantitative double blind user study shows that the presented approach improves the understanding of complex transparent surfaces significantly.

Section: Related Workmentioning

confidence: 99%

Smart Transparency for Illustrative Visualization of Complex Flow Surfaces

Carnecky

Fuchs

Mehl

et al. 2013

“…Many cognitive scientists and visual artists have suggested motion as visual cues for the perception of shape and depth [31]. In Chi and Lee's work [4], they orient strokes along the direction field to represent varied artistic effects.…”

Section: Animating Stroke Flow With Painterly Art Mapsmentioning

confidence: 99%

Stylized Rendering Using Samples of a Painted Image

Yan

Chi

Lee

et al. 2008

Abstract-We introduce a novel technique to generate painterly art maps (PAMs) for 3D nonphotorealistic rendering. Our technique can automatically transfer brushstroke textures and color changes to 3D models from samples of a painted image. Therefore, the generation of stylized images or animation in the style of a given artwork can be achieved. This new approach works particularly well for a rich variety of brushstrokes ranging from simple 1D and 2D line-art strokes to very complicated ones with significant variations in stroke characteristics. During the rendering or animation process, the coherence of brushstroke textures and color changes over 3D surfaces can be well maintained. With PAM, we can also easily generate the illusion of flow animation over a 3D surface to convey the shape of a model.

“…Their work was further extended by Chen et al [5] with a novel spatial TF to specify deformation in volume animations. Lum et al [27] presented an impressive visualization technique called Kinetic Visualization for creating motion along a surface to facilitate the understanding of static transparent 3D shapes. Weiskopf [47] described some relevant psychophysical and physiological findings to demonstrate the important role of color in the perception of motion.…”

Section: Animated Visualizationmentioning

confidence: 99%

Interactive Transfer Function Design Based on Editing Direct Volume Rendered Images

2007

Abstract-Direct volume rendered images (DVRIs) have been widely used to reveal structures in volumetric data. However, DVRIs generated by many volume visualization techniques can only partially satisfy users' demands. In this paper, we propose a framework for editing DVRIs, which can also be used for interactive transfer function (TF) design. Our approach allows users to fuse multiple features in distinct DVRIs into a comprehensive one, to blend two DVRIs, and/or to delete features in a DVRI. We further present how these editing operations can generate smooth animations for focus + context visualization. Experimental results on some real volumetric data demonstrate the effectiveness of our method.