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SIG Seminar: “Uncertainty-Driven Geometry Reconstruction and Understanding”
October 16, 2020 at 10:00 AM - 11:00 AM
Choosing suitable data representations is one of the most critical topics when installing machine learning on 3D data. This talk discusses several recent works on utilizing uncertainty as a lens to develop suitable data representations and formulations for geometry reconstruction and understanding. We will discuss both theoretical results and applications in multi-scan registration, pose estimation, and scene understanding.
Assistant professor, Computer Science, The University of Texas at Austin.
Qixing Huang is an assistant professor of Computer Science at The University of Texas at Austin. He obtained his Ph.D. in Computer Science from Stanford University. He was a research assistant professor at Toyota Technological Institute at Chicago before joining UT Austin. Dr. Huang’s research spans computer vision, computer graphics, and machine learning, and has been published extensively in top venues. In particular, his recent focus is on developing deep learning algorithms that leverage Big Data to solve core problems in computer vision, computer graphics, and computational biology. 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.