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.