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While playing a fundamental role in shape understanding, the medial axis is known to be sensitive to small boundary perturbations. Methods for pruning the medial axis are usually guided by some measure of significance. The majority of significance measures over the medial axes of 3D shapes are locally defined and hence unable to capture the scale of(More)
Figure 1: Effects of remote rendering using ASEMM. Figure(a)-(e) are original LOD models. Instead of constantly connecting the server for data updating when viewpoints change, our method makes the model split adaptively (Figure(f)(g)(i)) and transmission is only needed when the error of splitting extends to some limits(Figure(h)). We use this mechanism to(More)
Digital 3D models have emerged as a new type of multimedia following sound, image and video. This media type has been distributed and processed widely on desktop PCs. However, processing 3D models on mobile devices is more difficult, mainly due to their physical constraints. Though the remote rendering framework is able to make up for some deficiencies,(More)
Scenes in computer animation can have extreme complexity, especially when high resolution objects are placed in the distance and occupy only a few pixels. A useful technique for level of detail in these cases is to use a sparse voxel octree containing both hard surfaces and a participating medium consisting of microflakes. In this paper, we discuss three(More)
Medial axis is a classical shape descriptor that is widely used in computer graphics, computer vision, and pattern recognition. Defined elegantly as the locus of points with multiple nearest neighbors on the object boundary, the medial axis preserves both the structure and topology of the object in a compact form - a geometry that has one lower dimension(More)
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