Leo Guibas: 2017 Plenary Session


Tuesday, April 11, 2017
Location: McCaw Hall, Arrillaga Alumni Center

"Making 3D Data Universally Accessible and Useful"



Deep knowledge of the world is necessary if we are to have autonomous and intelligent agents and artifacts that can assist us or even carry out tasks entirely independently. One way to factorize the complexity of the world is to associate information and knowledge with stable entities, animate or inanimate, such as a person or a vehicle, etc. We aim to create and annotate reference representations for objects based on 3D models with the aim of delivering such information to new observations, as needed. In this object-centric view, the goal is to use these reference representations for aggregating information and knowledge about object geometry, appearance, articulation, materials, physical properties, affordances, and functionality. We acquire such information in a multitude of ways, both from crowd-sourcing and from establishing direct links between models and signals, such as images, videos, and 3D scans -- and through these to language and text. The purity of the 3D representation allows us to establish robust maps and correspondences for transferring information among the 3D models themselves -- making our current 3D repository, ShapeNet, a true network. This effectively enables us to add missing information to signals through computational imagination, giving us for example the ability to infer what an occluded part of an object in an image may look like, or what other object arrangements may be possible, based on the world-knowledge encoded in ShapeNet. Deep neural network architectures appropriate for operating directly on irregular 3D data, will be discussed, as well as ways to learn object function from observing multiple action sequences involving objects.


Leonidas Guibas obtained his Ph.D. from Stanford University under the supervision of Donald Knuth. His main subsequent employers were Xerox PARC, DEC/SRC, MIT, and Stanford. He is currently the Paul Pigott Professor of Computer Science (and by courtesy, Electrical Engineering) at Stanford University. He heads the Geometric Computation group and is part of the AI Laboratory, the Graphics Laboratory, the Bio-X Program, and the Institute for Computational and Mathematical Engineering. Professor Guibas' interests span geometric data analysis, computational geometry, geometric modeling, computer graphics, computer vision, robotics, ad hoc communication and sensor networks, and discrete algorithms. Some well-known past accomplishments include the analysis of double hashing, red-black trees, the quad-edge data structure, Voronoi-Delaunay algorithms, the Earth Mover's distance, Kinetic Data Structures (KDS), Metropolis light transport, heat-kernel signatures, and functional maps. Professor Guibas is a member of the National Academy of Engineering, an ACM Fellow, an IEEE Fellow and winner of the ACM Allen Newell award and the ICCV Helmholtz prize.