Guest speaker: Vladimir G. Kim, Adobe Research (Creative Technologies Lab)
Title: Data-driven Geometry Processing
Many geometry processing tasks require understanding geometric data from human perspective. Recent growth of large online repositories of 3D shapes and advances in machine learning enable building data models that can be used to understand similarities, variations, semantics, and functionality of 3D objects. There two main challenges in using machine learning for geometry processing. First, we need to collect human annotations to learn functional and semantic attributes of shapes. And second, we need to develop novel geometric representations that are compatible with state-of-the-art machine learning algorithms. To address the first challenge, we developed several techniques that significantly reduce the cost of human supervision by combining several types of crowd-sourced worker tasks, automatic label propagation, and meta-data that comes for free with the 3D models. In the second part of my talk I will discuss the challenges, existing solutions, and open research problems in geometric representations for machine learning.
Vladimir G. Kim is a Research Scientist at Adobe. His interests include geometry processing and analysis of shapes and collections of 3D models. Before joining Adobe Research in 2015 he was a postdoctoral scholar at Stanford University. Prior to that, Vladimir received his PhD from Princeton University in 2013, and undergraduate degree from Simon Fraser University. Vladimir is a recipient of the Siebel Scholarship and the NSERC Postgraduate Scholarship. He was also on the International Program Committee for SIGGRAPH, Symposium on Geometry Processing, Pacific Graphics, and CAD/Graphics.