Hui-Juan Zhang (张慧娟)

I am a third year Ph.D Student in Peking University, under supervision of Prof. Guo-Ping Wang. Before that, I received my bachelor degree from Central South University in 2009. I'm now working in the Graphics and Interactive Technology Lab.

My research interests include: shape modeling, geometry processing, feature detection, etc

My contact info:
Email: zhanghj@graphics.pku.edu.cn
Phone: 86-10-62765819-823 (Office)
Address: Science Building No.1 Room 1321, Peking University, Beijing, P.R. China, P.C: 100871.

Abstract: Feature detection and extraction plays an important role in mesh editing. At present, most existing algorithms often fail in dealing with irregular meshes. To overcome those problems, an algorithm for extracting feature edges of triangle meshes based on tensor voting is presented. First, all vertices of the input mesh are classified according to the observation that there is a close correspondence between eigenvalue’s distribution of tensor voting matrix and geometrical features; The classified vertices are optimized by connecting breakpoints; A region growing is performed for each seed triangle and the boundaries of the regions are extracted as the edges. The experiment results show that the proposed algorithm is effective in nearly all cases, such as models with non-uniformly distributed triangles, long and narrow triangles or with holes. And it is insensitive to noisy data.[Pdf][Bib]

Abstract: Poisson disk sampling has been widely used in many applications such as remeshing, procedural texturing, object distribution, illumination, etc. While 2D Poisson disk sampling is intensively studied in recent years, direct Poisson disk sampling on 2-manifold surface is rarely covered. In this paper, we present a novel framework which generates approximate Poisson disk distribution directly on mesh, a discrete representation of 2-manifold surfaces. Our framework is easy to implement and provides extra exibility to specied sampling issues like feature-preserving sampling and adaptive sampling. We integrate the tensor voting method into feature detection and adaptive sample radius calculation. Remeshing as a special downstream application is also addressed. According to our experiment results, our framework is ecient, robust, and widely applicable.[Pdf] [Bib]

China hewlett-packard excellent students scholarship

National first-class scholarship.

Mittal outstanding student award

Excellent party members of university in hunan province

The best team of the year

China hewlett-packard excellent students scholarship

National first-class scholarship.