Yingcai Wu
Zhejiang University
Title: Spatio-temporal data visualization
Short bio:
Yingcai Wu is a National Youth-1000 scholar and a ZJU100 Young Professor at the State Key Lab of CAD & CG, Zhejiang University. He obtained his Ph.D. degree in Computer Science from the Hong Kong University of Science and Technology (HKUST). Prior to his current position, Yingcai Wu was a researcher in the Microsoft Research Asia, Beijing, China from 2012 to 2015, and a postdoctoral researcher at the University of California, Davis from 2010 to 2012.
His main research interests are in visual analytics, information visualization, and human-computer interaction, with focuses on urban computing, social media analysis, text visualization, and behavior analysis. He has published more than 50 refereed papers, including 21 IEEE TVCG papers. His three papers have been awarded Honorable Mention at IEEE VIS (SciVis) 2009, IEEE VIS (VAST) 2014, and IEEE PacificVis 2016. For more information, visit www.ycwu.org
Yingcai was one of the Papers Co-Chairs of IEEE Pacific Visualization 2017, ChinaVis 2016, ChinaVis 2017, and VINCI 2014. He is/was also the guest editor of IEEE Transactions on Visualization and Computer Graphics (TVCG), ACM Transactions on Intelligent Systems and Technology (TIST), and IEEE Transactions on Multimedia. He was in the Program Committee for InfoVis 2017, 2016, 2015, SciVis 2017, 2016, 2015, 2014, EuroVis 2015, 2016, 2017, PacificVis 2011, 2012, 2013, 2015, 2016.
Yunhai Wang
Shandong University
Title: Task-driven Automated Data Visualization
Abstract:
By providing visual representation of data, visualization can help people carry out some tasks more effectively. Given a data set, however, there are have too many different visualization techniques, where each technique has many parameters to be tweaked. We are asking if it is possible to automatically design a visualization that is best suited to pursue a given task on given input data. We have developed five new techniques to achieve this goal for specific data sets: perception-driven dimensionality reduction, the selection of line chart or scatter plot for time-series data, a framework for aspect ratio selection, consistency-preserving word cloud editing and constrained graph exploration.
Short bio: Yunhai Wang is an associate professor in School of Computer Science and Technology at Shandong University. His interests include scientific visualization, information visualization and 3d shape analysis, focusing specifically on automated data visualization.
Lei Shi
the State Key Laboratory of Computer Science
Institute of Software, Chinese Academy of Sciences
Title: Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation
Abstract: Visually comparing human brain networks from multiple population groups serves as an important task in the field of brain connectomics. The commonly used brain network representation, consisting of nodes and edges, may not be able to reveal the most compelling network differences when the reconstructed networks are dense and homogeneous. In this paper, we leveraged the block information on the Region Of Interest (ROI) based brain networks and studied the problem of blockwise brain network visual comparison. An integrated visual analytics framework was proposed. In the first stage, a two-level ROI block hierarchy was detected by optimizing the anatomical structure and the predictive comparison performance simultaneously. In the second stage, the NodeTrix representation was adopted and customized to visualize the brain network with block information. We conducted controlled user experiments and case studies to evaluate our proposed solution. Results indicated that our visual analytics method outperformed the commonly used node-link graph and adjacency matrix design in the blockwise network comparison tasks. We have shown compelling findings from two real-world brain network data sets, which are consistent with the prior connectomics studies.
Short bio: Lei Shi is an associate professor in the State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences. Before that, he was a research staff member and research manager at IBM Research - China. He holds B.S. (2003), M.S. (2006) and Ph.D. (2008) degrees from Department of Computer Science and Technology, Tsinghua University. His current research interests are Visual Analytics and Data Mining, with more than 70 papers published in top-tier venues, such as IEEE TVCG, TKDE, TC, VIS, ICDE, ICDM, Infocom, ACM Sigcomm and CSCW. He is the recipient of IBM Research Division Award on "Visual Analytics" and the IEEE VAST Challenge Award twice in 2010 and 2012. He has organized several workshops on combining visual analytics and data mining, in ICDM, CIKM, etc, and served on the program committees of many related conferences such as KDD, Graph Drawing, etc. He is an IEEE senior member.
Jin Huang
Zhejiang University
Title: Hex Remeshing
Abstract: Hex mesh has important applications in graphics and mechanics. In this talk, we will show how to address some key challenges in hex remeshing, the “holy grail” problem in remeshing, and discuss the possible future works.
Short bio: Prof. Jin Huang got the Ph.D degree at 2007 from Zhejiang university. He visited Caltech from 2012 to 2014, and was awarded Excellent Ph.D thesis of China Computer Federation at 2008 and the NSFC Excellent Young Scholars Program at 2015. His research interests are mainly in the areas of geometry processing and physically based simulation. He has served as PC of SIGGRAPH Asia, SGP, SCA etc. conference, associated editor of CAGD and program co-chair of Geometric Modeling and Processing (GMP) 2016.
Ligang Liu
University of Science and Technology of China
Title: Smooth Assembled Mappings for Large-Scale Real Walking
Short bio: Ligang Liu is a professor at the School of Mathematical Sciences, University of Science and Technology of China. He received his B.Sc. (1996) and his Ph.D. (2001) from Zhejiang University, China. Between 2001 and 2004, he worked at Microsoft Research Asia. Then he worked at Zhejiang University during 2004 and 2012. He paid an academic visit to Harvard University during 2009 and 2011. His research interests include digital geometric processing, computer graphics, and image processing. He serves as the associated editors for journals of IEEE Transactions on Visualization and Computer Graphics, IEEE Computer Graphics and Applications, Computer Graphics Forum, Computer Aided Geometric Design, and The Visual Computer. He served as the conference co-chair of GMP 2017 and the program co-chairs of GMP 2018, CAD/Graphics 2017, CVM 2016, SGP 2015, and SPM 2014. His research works could be found at his research website: http://staff.ustc.edu.cn/~lgliu
Bin Wang
Tsinghua University
Title：Gauss Surface Reconstruction
Abstract: In this paper, we introduce a surface reconstruction method that can perform gracefully with non-uniformly-distributed, noisy, and even sparse data. We reconstruct the surface by estimating an implicit function and then obtain a triangle mesh by extracting an iso-surface from it. Our implicit function takes advantage of both the indicator function and the signed distance function. It is dominated by the indicator function at the regions away from the surface and approximates (up to scaling) the signed distance function near the surface. On one hand, it is well defined over the entire space so that the extracted iso-surface must lie near the underlying true surface and is free of spurious sheets. On the other hand, thanks to the nice properties of the signed distance function, a smooth iso-surface can be extracted using the approach of marching cubes with simple linear interpolations. More importantly, our implicit function can be estimated directly from an explicit integral formula without solving any linear system. This direct approach leads to a simple, accurate and robust reconstruction method, which can be paralleled with little overhead. We call our reconstruction method Gauss surface reconstruction. We apply our method to both synthetic and real-world scanned data and demonstrate the accuracy, robustness and efficiency of our method. The performance of Gauss surface reconstruction is also compared with that of several state-of-the-art methods.
Short bio: Bin Wang is currently an associate professor at School of Software, Tsinghua University, China. He received his B.Sc. degree in Chemistry in 1999, and his Ph.D. in Computer Science from Tsinghua University in 2005. He was a research assistant at Department of Computer Science, Hong Kong University, and had postdoctoral research training at ISA/ALICE Research Group, INRIA-LORIA, France. His research interests include geometry processing, and image and video processing.
Dong-Ming Yan
National Laboratory of Pattern Recognition of the Institute of Automation
Chinese Academy of Sciences
Title: Isotropic Surface Remeshing without Large and Small Angles
Abstract: We introduce a novel algorithm for isotropic remeshing which progressively eliminates obtuse triangles and improves small angles. The main novelty of the proposed approach are a simple vertex insertion scheme that facilitates the removal of obtuse angles, and a vertex removal operation that improves the distribution of small angles. Combined with other standard local mesh operators, e.g., connectivity optimization and local tangential smoothing, our algorithm is able to remesh a low quality mesh surface efficiently. Our approach can be used as a post-processing step following other remeshing approaches or applied directly. Our method has a similar computational efficiency compared to the fastest approach available, i.e., real-time adaptive remeshing. Compared with state-of-the-art approaches, our method consistently generates better results evaluated by different metrics.
Short bio:Dong-Ming Yan received his PhD degree from Hong Kong University in 2010, and his Bachelor's and Master's degrees from Tsinghua University in 2002 and 2005, respectively. He is an associate professor at the National Laboratory of Pattern Recognition of the Institute of Automation, Chinese Academy of Sciences. His research interests include computer graphics, geometric processing and visualization.
Liang Wan
Tianjin University
Title: Panoramic Image Processing
Abstract: Panorama is an effective and convenient way to represent large filed-of-view environment. With the rapid development of image stitching and panorama camera technologies, panoramas have been becoming prevalent. A spherical panorama owns characteristics differing it from normal 2D images. It covers 360-degree environment and defined on the sphere domain.
We have exploited panoramic image processing in depth from several aspects. To be specific, we have focused on the virtual navigation between panoramas in real-time (TMM 2013), addressed the problem of rapid and robust feature detection and matching for panoramas (IJCV 2015), and developed a spherical superpixel segmentation method (ICME 2016, TMM with major revision).
Short bio: Liang Wan is an Associate Professor in the School of Computer Software, Tianjin University, China. She received her Ph.D. degree in Computer Science and Engineering from The Chinese University of Hong Kong (CUHK) in 2007. Her research interest is mainly on intelligent image synthesis, including image-based rendering, image navigation, pre-computed lighting, and panoramic image processing. She has published journal papers in ACM TOG, IJCV, TVCG, TMM, Patter Recognition et al, and conference papers in Siggraph Asia, CVPR, ICCV, ICME, CGI. She is IEEE member, and has served in program committee of several conferences, including Siggraph Asia Technical Sketches & Posters 2011, ChinaVis 2015/2016, CAG&CD 2017.