Guo, Xiaohu
Permanent URI for this collectionhttps://hdl.handle.net/10735.1/2391
Dr. Guo works in the areas of computer graphics, computer animation and simulation, geometric and physically-based modeling, computer animation, virtual reality and medical imaging. More information about Xiaohu Guo is available on his home and Research Explorer pages.
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Browsing Guo, Xiaohu by Author "Wang, W."
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Item Field-Aligned and Lattice-Guided Tetrahedral Meshing(John Wiley & Sons Ltd.) Ni, Saifeng; Zhong, Z.; Huang, J.; Wang, W.; Guo, Xiaohu; Ni, Saifeng; Guo, XiaohuWe present a particle-based approach to generate field-aligned tetrahedral meshes, guided by cubic lattices, including BCC and FCC lattices. Given a volumetric domain with an input frame field and a user-specified edge length for the cubic lattice, we optimize a set of particles to form the desired lattice pattern. A Gaussian Hole Kernel associated with each particle is constructed. Minimizing the sum of kernels of all particles encourages the particles to form a desired layout, e.g., field-aligned BCC and FCC. The resulting set of particles can be connected to yield a high quality field-aligned tetrahedral mesh. As demonstrated by experiments and comparisons, the field-aligned and lattice-guided approach can produce higher quality isotropic and anisotropic tetrahedral meshes than state-of-the-art meshing methods.Item Q-Mat+: An Error-Controllable and Feature-Sensitive Simplification Algorithm for Medial Axis Transform(Elsevier B.V.) Pan, Y.; Wang, B.; Guo, Xiaohu; Zeng, H.; Ma, Y.; Wang, W.; Guo, XiaohuThe medial axis transform (MAT), as an intrinsic shape representation, plays an important role in shape approximation, recognition and retrieval. Q-MAT is a state-of-the-art algorithm driven by quadratic error minimization to compute a geometrically precise, structurally concise, and compact representation of the MAT for 3D shapes. In this work we extend the technique to make it more robust, controllable, and name it Q-MAT+. Combining shape diameter function (SDF) and other mesh information, Q-MAT+ gets a more complete and accurate initial MAT than Q-MAT, even for extreme thin features, such as wires and sheets. Q-MAT+ could quickly remove insignificant branches while preserving significant ones to get a simple and faithful piecewise linear approximation of the MAT. Moreover, it performs the medial axis simplification with explicit maintenance and the control of Hausdorff error, which is not originally supported in Q-MAT. We further demonstrate the outstanding efficiency and accuracy of our method compared with other existing approaches for MAT generation and simplification. ©2019 Elsevier B.V.