Namdar Homayounfar
I'm a research scientist at Uber ATG Toronto and a PhD student at Univeristy of Toronto under the supervision of Prof. Raquel Urtasun. I have broad research interests in deep learning and computer vision. My current focus is in development of deep structured models for the creation of HD maps required for the safe navigation of autonomous vehicles. Previously, I obtained my MSc degree in Statistics at University of Toronto and prior to that my BSc in Probalblity and Statistics from McGill University.
Research Papers
Convolutional Recurrent Network for Road Boundary Extraction
J. Liang, N. Homayounfar, S. Wang, W.-C. Ma, R. Urtasun
Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper, we tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper, we tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Deep Multi-Sensor Lane Detection
M. Bai, G. Mattyus, N. Homayounfar, S. Wang, S. K. Lakshmikanth, R. Urtasun
Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or videos) as input and reason in image space. In this paper we argue that accurate image estimates do not translate to precise 3D lane boundaries, which are the input required by modern motion planning algorithms. [...] [PDF]
International Conference on Intelligent Robots and Systems (IROS), 2018
Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or videos) as input and reason in image space. In this paper we argue that accurate image estimates do not translate to precise 3D lane boundaries, which are the input required by modern motion planning algorithms. [...] [PDF]
International Conference on Intelligent Robots and Systems (IROS), 2018
Hierarchical Recurrent Attention Networks for Structured Online Maps
N. Homayounfar, W. Ma, S. Lakshmikanth, R. Urtasun
In this paper, we tackle the problem of online road network extraction from sparse 3D point clouds. Our method is inspired by how an annotator builds a lane graph, by first identifying how many lanes there are and then drawing each one in turn. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018
In this paper, we tackle the problem of online road network extraction from sparse 3D point clouds. Our method is inspired by how an annotator builds a lane graph, by first identifying how many lanes there are and then drawing each one in turn. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018
Sports Field Localization via Deep Structured Models
N. Homayounfar, S. Fidler, R. Urtasun
In this work, we propose a novel way of efficiently localizing a soccer field from a single broadcast image of the game. Related work in this area relies on manually annotating a few key frames and extending the localization to similar images, or installing fixed specialized cameras in the stadium from which the layout of the field can be obtained. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017
In this work, we propose a novel way of efficiently localizing a soccer field from a single broadcast image of the game. Related work in this area relies on manually annotating a few key frames and extending the localization to similar images, or installing fixed specialized cameras in the stadium from which the layout of the field can be obtained. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017
