Eric Kee
Eric received his Ph.D. with advisor Hany Farid at Dartmouth College, where he studied physics-based vision and its applications to image forensics. As a postdoc, Eric studied computational imaging under advisor Shree Nayar at Columbia University. Following Columbia, Eric joined the Facebook computational imaging group, and a graphics & simulation startup, Avametric, with Prof. James O’Brien, U.C. Berkeley where he developed methods for fitting deformable models of human bodies for virtual clothing try-on. Eric joined the Uber ATG perception group in 2016, then led by Prof. Drew Bagnel of CMU, before the first public launch of self-driving vehicles in Pittsburgh. In his work at ATG, Eric (and coauthors) developed and deployed the first deep neural network architecture for object detection to run on Uber’s self-driving fleet. Eric is currently a member of the Uber ATG R&D group, led by Raquel Urtasun. His research interests include self-driving, machine learning, and biological vision.
Research Papers
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
G. P. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez, C. Wellington
In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact. [...]
[PDF]
Computer Vision and Pattern Recognition (CVPR), 2019
In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact. [...]
[PDF]
Computer Vision and Pattern Recognition (CVPR), 2019
DeepSignals: Predicting Intent of Drivers Through Visual Attributes
D. Frossard, E. Kee, R. Urtasun
Detecting the intention of drivers is an essential task in self-driving, necessary to anticipate sudden events like lane changes and stops. Turn signals and emergency flashers communicate such intentions, providing seconds of potentially critical reaction time. In this paper, we propose to detect these signals in video sequences by using a deep neural network that reasons about both spatial and temporal information. [...] [PDF]
International Conference on Robotics and Automation (ICRA), 2019
Detecting the intention of drivers is an essential task in self-driving, necessary to anticipate sudden events like lane changes and stops. Turn signals and emergency flashers communicate such intentions, providing seconds of potentially critical reaction time. In this paper, we propose to detect these signals in video sequences by using a deep neural network that reasons about both spatial and temporal information. [...] [PDF]
International Conference on Robotics and Automation (ICRA), 2019