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Jingqi Yan

Publications and source records attributed to Jingqi Yan.

2 recordsLinked to original sources

Mesh simplification with hierarchical shape analysis and iterative edge contraction.

This paper presents a novel mesh simplification algorithm. It decouples the simplification process into two phases: shape analysis and edge contraction. In the analysis phase, it imposes a hierarchical structure on a surface mesh by uniform hierarchical partitioning, marks the importance of each vertex in the hierarchical structure, and determines the affected regions of each vertex at the hierarchical levels. In the contraction phase, it also divides the simplification procedure into two steps: half-edge contraction and optimization. In the first step, memoryless quadric metric error and the importance of vertices in the hierarchical structure are combined to determine one operation of half-edge contraction. In the second step, it repositions the vertices in the half-edge simplified mesh by minimizing the multilevel synthesized quadric error on the corresponding affected regions from the immediately local to the more global. The experiments illustrate the competitive results.

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Mesh parameterization by minimizing the synthesized distortion metric with the coefficient-optimizing algorithm.

The parameterization of a 3D mesh into a planar domain requires a distortion metric and a minimizing process. Most previous work has sought to minimize the average area distortion, the average angle distortion, or a combination of these. Typical distortion metrics can reflect the overall performance of parameterizations but discount high local deformations. This affects the performance of postprocessing operations such as uniform remeshing and texture mapping. This paper introduces a new metric that synthesizes the average distortions and the variances of both the area deformations and the angle deformations over an entire mesh. Experiments show that, when compared with previous work, the use of synthesized distortion metric performs satisfactorily in terms of both the average area deformation and the average angle deformation; furthermore, the area and angle deformations are distributed more uniformly. This paper also develops a new iterative process for minimizing the synthesized distortion, the coefficient-optimizing algorithm. At each iteration, rather than updating the positions immediately after the local optimization, the coefficient-optimizing algorithm first update the coefficients for the linear convex combination and then globally updates the positions by solving the Laplace system. The high performance of the coefficient-optimizing algorithm has been demonstrated in many experiments.

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