Shape-aware Volume Illustration
Wei Chen, Aidong Lu, David S.Ebert
Journal of Computer Graphics Forum (Proceedings of Eurographics 2007), 26(3), Czech Republic, September.2007, pp.705-714

We introduce a novel volume illustration technique for regularly sampled volume datasets. The fundamental difference between previous volume illustration algorithms and ours is that our results are shape-aware, as they depend not only on the rendering styles, but also the shape styles. We propose a new data structure that is derived from the input volume and consists of a distance volume and a segmentation volume. The distance volume is used to reconstruct a continuous field around the object boundary, facilitating smooth illustrations of boundaries and silhouettes. The segmentation volume allows us to abstract or remove distracting details and noise, and apply different rendering styles to different objects and components. We also demonstrate how to modify the shape of illustrated objects using a new 2D curve analogy technique. This provides an interactive method for learning shape variations from 2D hand-painted illustrations by drawing several lines. Our experiments on several volume datasets demonstrate that the proposed approach can achieve visually appealing and shape-aware illustrations. The feedback from medical illustrators is quite encouraging.

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Adaptive Patch-based Mesh Fitting for Reverse Engineering
Hongwei Lin, Wei Chen, Hujun Bao
Computer-Aided Design


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Conservative Voxelization
Long Zhang,Wei Chen, David S.Ebert, Qunsheng Peng
The Visual Computer (Special Issue of Proceedings of Computer Graphics International 2007)


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Precomputing Data-driven Tree Animation
Long Zhang, Yubo Zhang, Zhongding Jiang, Luyin Li, Wei Chen, Qunsheng Peng
Journal of Computer Animation and Virtual Worlds (Special Issue of Proceedings of CASA 2007)


We present a data-driven approach that synthesizes tree animations from a set of pre-computed motion data. Our approach improves previous motion synthesis algorithms for character animation in several aspects. We first introduce a simple yet effective sampling scheme to generate a rich and reusable motion database for each tree model.We also propose a novel technique to generate a fine set of transitions that are uniformly distributed in the motion database. The transition lengths are adaptively determined according to the similarity of the transiting frame pairs. In the runtime, we employ a greedy searching algorithm to synthesize smooth tree animations under an adjustable wind condition. Experimental results show that our approach achieves comparable quality to physically based methods, while in orders of magnitude faster performance.

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