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We study Computer Vision and Robotics, focusing on the computational principles underlying Artificial Intelligence. We are interested in building robots that automatically understand and interact with the physical worlds, both inferring the semantics and extracting 3D structure.

We design end-to-end algorithms to learn deep 3D representations from big 3D data for visual scene understanding. We believe that it is critical to consider the role of a machine as an active explorer in a 3D world, such as a robot, and learn from rich 3D data close to the natural input to human visual system.

Specifically, our group is at the frontier of 3D Deep Learning, RGB-D Recognition and Reconstruction, Deep Learning for Robotics, Place-centric 3D Context Representation, Synthesis for Analysis, Big Data Robotics, Autonomous Driving, Robot Learning, Large-scale Crowd-sourcing, and Petascale Big Data.


We gratefully acknowledge the generous support of Intel, Amazon, Ford, ABB, National Science Foundation, Google, MERL, Facebook, Microsoft, and NVIDIA for our research.