I'm a PhD of Computing in the University of Utah
with my interest focused on Computer Graphics, Computer Vision. More specifically, in high accuracy motion capture technology.
I am very proud of the next generate motion capture system I created with Ladislav Kavan , my current advisor.
I have interdisciplinary education backgrounds,
which covers electric engineering, mathematics and computer science.
I am ranked top ranked student in computational mathematics major of School of Mathematics
in DLUT. I won the title of Outstanding Graduate and Outstanding Master Graduation Thesis in my school.
I have been maintaining a open source mesh processing library:MeshFrame,
as a main contributor for over a year.
This is a lightweight, efficient, header-only mesh processing framework with better efficiency superior to other
state-of-the-art libraries. It supports dynamic mesh structure editing, supports runtime dynamic properties,
supports triangle/tetrahedral mesh, with a built-in viewer, and also includes a number of implementations of mesh processing algorithms.
Bachelor of Electrical Engineering and Its Automated | : Hunan University |
Master of Computational Mathematics | : Dalian University of Technology |
PhD Student in Computing | : University of Utah |
My current research topics include:
Multi-Normal Estimation via Pair Consistency Voting
IEEE Transactions on Visualization and Computer Graphics(TVCG)
[Page],
[PDF].
Jie Zhang, Junjie Cao (co-first authors), Xiuping Liu, He Chen, Bo Li, Ligang Liu
This paper presents a unified definition for point cloud normal of feature and non-feature points, which allows feature points to possess multiple normals. This definition facilitates several succeeding operations, such as feature points extraction and point cloud filtering. We also develop a feature preserving normal estimation method which outputs multiple normals per feature point. In addition, we introduce an error measure compatible with traditional normal estimators, and present the first benchmark for normal estimation, composed of 152 synthesized data with various features and sampling densities, and 288 real scans with different noise levels.
Normal Estimation via Shifted Neighborhood for point cloud
Journal of Computational and Applied Mathematics
[Page],
[PDF].
Junjie Cao, He Chen, Jie Zhang, Yujiao Li, Xiuping Liu, Changqing Zou
We present a fast and quality normal estimator based on neighborhood shift. Instead of using the neighborhood centered at the point, we wish to locate a neighborhood containing the point but clear of sharp features, which is usually not centering at the point. Two specific neighborhood shift techniques are designed in view of the complex structure of sharp features and the characteristic of raw point clouds.
Online Knapsack Problem Under Concave Functions
Frontiers in Algorithmics
[Page],
[PDF].
Xin Han, Ning Ma, Kazuhisa Makino, He Chen
In this paper, we address an online knapsack problem under concave function $f ( x )$, i.e., an item with size x has its profit $f ( x )$. We first obtain a simple lower bound $\max \{q, \frac{f'(0)}{f(1)}\}$ , where $q \approx 1.618$ , then show that this bound is not tight, and give an improved lower bound. Finally, we find the online algorithm for linear function can be employed to the concave case, and prove its competitive ratio is $\frac{f'(0)}{f(1/q)}$ , then we give a refined online algorithm with a competitive ratio $\frac{f'(0)}{f(1)} +1$ . And we also give optimal algorithms for some piecewise linear functions.
Mesh saliency detection via double absorbing Markov chain in feature space
The Visual Computer
[Page],
[PDF].
Xiuping Liu, Pingping Tao, Junjie Cao, He Chen, Changqing Zou
We propose a mesh saliency detection approach using absorbing Markov chain. Unlike most of the existing methods based on some center-surround operator, our method employs feature variance to obtain insignificant regions and considers both background and foreground cues.
MeshFrame: An efficient header-only mesh processing library [Link]
A open source mesh processing library I developed and maintained as a major contributor, which is a lightweight, efficient, header-only mesh processing framework. Its speed is superior to other state-of-the-art libraries like OpenMesh, MeshLab or CGAL. It supports dynamic mesh structure editing, supports runtime dynamic properties, supports triangle/tetrahedral mesh, with a built-in viewer, and also includes a large number of mesh processing algorithms.
3DFace:A cross-platform face reconstruction application [Link]
I developed a 3D face reconstruction algorithm using a depth camera. Users can be allowed to automatically capture facial data in the process of rotating face in front of the camera, and use multi-frame alignment technology to merge the geometric and texture data from each angle of the human face, and outputs a more complete human face model in a short peroid of time. Both the mobile and the PC versions of this algorithm have been implemented. On PC, the procedure takes 300ms, on mobile phone the procedure takes 3s.
3D scanner data processing software
Developed a software for a 3D scanner, to support point cloud denoising, point cloud manual editing, point cloud alignment, SLAM global optimization, point cloud reconstruction, feature recovery, mesh simplification and other functions, supporting scan data with up to tens of millions of point. In the software development team I am responsible for the implementation of all point cloud processing core algorithms.
Mesh Simplification
This is a mesh simplification algorithm developed as an application of MeshFrame. The algorithm is based on Quadric Error Metric (QEM). We make use of half-edge collapse method for mesh simplification and modify the QEM to solve the break between different texture coordinates. As far as we know, this is the fastest implementation of QEM based mesh simplification algorithm, even faseter than the one implemented by MeshLab, which is not based on half-edge structure.
The Wing Surface Modeling and Gridding
Generate the wing surface using B-spline parametric surfaces, mastered the method of parametric surfaces for surface subdivision, and learned to use parametric surface processing software Gmsh.
The Smart Car Competition
I took part in the smart car competition. In my group, I mainly in charge of the track detecting and controlling algorithm. I learned a lot knowledge about automatic control, embeded system and image processing algorithm.