Center for Research in Comptuer Vision
Center for Research in Comptuer Vision
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Seminar Announcement

3D-Centric Data-Driven Visual Computing

Dr. Qixing Huang of the University of Texas

Monday, November 20, 2017 · 4:00PM · HEC 101

Abstract
Over the past decade, the quantity of accessible visual data has undergone unprecedented expansion. This data is not only vast but also exists within numerous modalities such as images, videos, and 3D models. While researchers have aptly exploited the inflation of these first two areas, the significant growth in 3D data has been predominantly overlooked. This talk conveys two messages. First, the availability of big 3D data enables us to accomplish many previously hard or even impossible tasks in 3D visual computing. Second, to ultimately solve 3D visual computing problems, merely utilizing 3D data is insufficient, and it is vital to incorporate data from other domains. I will discuss a series of works under this big picture, including data-driven visual correspondences, joint learning of neural networks and learning 3D representations from image data.

Biography
Qixing Huang is an assistant professor at the University of Texas at Austin. He obtained his PhD in Computer Science from Stanford University and his MS and BS in Computer Science from Tsinghua University. He was a research assistant professor at Toyota Technological Institute at Chicago before joining UT Austin. He has also worked at Adobe Research and Google Research, where he developed some of the key technologies for Google Street View. Dr. Huang’s research spans the fields of computer vision, computer graphics, and machine learning. In particular, he is interested in designing new algorithms that process and analyze big geometric data (e.g., 3D shapes/scenes). He is also interested in statistical data analysis, compressive sensing, low-rank matrix recovery, and large-scale optimization, which provides theoretical foundation for his research. Qixing has published extensively at SIGGRAPH, CVPR and ICCV, and has received grants from NSF and various industry gifts. He also received the best paper award at the Symposium on Geometry Processing 2013.