Faculty Candidate Seminar

Perceiving the 3D World from Images and Videos

Yu Xiang

Post Doc
University of Washington
Thursday, March 15, 2018
10:30am - 11:30am
3725 Beyster

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About the Event

With recent advances in artificial intelligence, we have witnessed the deployment of AI systems that are capable of improving our daily lives such as the Amazon checkout-free shop and self-driving cars. However, deploying a personal robot that is able to assist people in accomplishing real world tasks is still very challenging. The difficulty lies in the complexity of the 3D world we live in, where a robot may encounter thousands of objects, different scenes and human activities. For a robot to safely operate in such an environment, it needs to effectively extract, represent and interpret information about the 3D environment from different sensory data.

In this talk, I will present my efforts towards designing intelligent visual models that perceive the 3D world from images and videos. I will start by describing a novel 3D scene understanding framework that jointly reconstructs the geometry of a scene and recognizes objects in the scene. Then, I will elaborate on the design of a new convolutional neural network for recognizing the 3D location and 3D pose of objects in cluttered scenes. The network is very robust to occlusions between objects and handles symmetric objects elegantly. I will conclude this talk by demonstrating that our methods for 3D object recognition and scene understanding provide useful information for intelligent systems to conduct tasks in the real world such as in autonomous driving and robot manipulation.


Yu Xiang is a postdoctoral researcher in the Robotics Research Lab at Nvidia. He received his Ph.D. in electrical engineering from the University of Michigan at Ann Arbor in 2016 advised by Prof. Silvio Savarese. He was a postdoctoral researcher with Prof. Dieter Fox in Computer Science & Engineering at the University of Washington from 2016 to 2017 and was a visiting student researcher in the artificial intelligence lab at Stanford University from 2013 to 2016. He received M.S. degree and B.S. degree both in computer science from Fudan University in 2010 and 2007, respectively. His research interests primarily focus on computer vision and perception for robotics, with emphasize on studying how can an intelligent system or a robot understand its 3D environment from sensing.

Additional Information

Sponsor(s): CSE

Open to: Public