News & Events
Understanding the 3D Environments for Interactions
Release time:April 25, 2019

Topic: Understanding the 3D Environments for Interactions

Speaker: Professor Hao Su, UC San Diego

Time: 14:00-16:00 PM, April 26

Venue: G610, New Main Building


Being able to understand the surrounding in both geometry and physics attributes as we humans do is a key step for building intelligent autonomous agents. This talk will cover a series of research progress in my lab towards this direction, focusing on how machine learning, especially deep learning, can be used to address challenging problems in 3D reconstruction, semantic recognition, and mobility structure induction. In particular, I will focus on the understanding of object parts. Object parts are handles of actionable information for interaction purposes. Knowing such object part structure and being able to assemble actionable information on parts is thus fundamentally important. I will show how this goal may be achieved by crowd-sourcing as well as algorithmic induction efforts from daily observations. The content in the talk is based upon latest papers published in SIGGRAPH Asia 2018 and CVPR 2019.

Biography of the Speaker:

Hao Su has been in UC San Diego as Assistant Professor of Computer Science and Engineering since July 2017. He is affiliated with the Contextual Robotics Institute and Center for Visual Computing. He served on the program committee of multiple conferences and workshops on computer vision, computer graphics, and machine learning. He is the Area Chair of ICCV’19, CVPR’19, IPC of Pacific Graphics'18, Program Chair of 3DV'17, Publication Chair of 3DV'16, and chair of various workshops at CVPR, ECCV, and ICCV. He is also invited as keynote speakers at workshops and tutorials in NIPS, CVPR, 3DV and CVPR, S3PM, etc. Professor Su is interested in fundamental problems in broad disciplines related to artificial intelligence, including machine learning, computer vision, computer graphics, robotics, and smart manufacturing. His work of ShapeNet, PointNet series, and graph CNNs have significantly impacted the emergence and growth of a new field, 3D deep learning. He used to work on ImageNet, a large-scale 2D image database, which is important for the recent breakthrough of computer vision. Applications of Su's research include robotics, autonomous driving, virtual/augmented reality, smart manufacturing, etc.

School of Computer Science and Engineering