Namdar Homayounfar
Namdar is a Research Scientist at Uber ATG R&D and a PhD student at the University of Toronto under the supervision of Professor Raquel Urtasun. He has broad research interests in deep learning and computer vision. Namdar’s current focus is in development of deep structured models for the creation of HD maps required for the safe navigation of autonomous vehicles. He obtained his MSc degree in statistics at the University of Toronto and, before that, a BSc in probability and statistics from McGill University.
Recent publications
LevelSet R-CNN: A Deep Variational Method for Instance Segmentation
Namdar Homayounfar, Yuwen Xiong, Justin Liang, Wei-Chiu Ma, Raquel Urtasun (ECCV 2020)
PolyTransform: Deep Polygon Transformer for Instance Segmentation
Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Yuwen Xiong, Rui Hu, Raquel Urtasun (CVPR 2020)
Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization
Wei-Chiu Ma*, Ignacio Tartavull*, Ioan Andrei Bârsan*, Shenlong Wang*, Min Bai, Gellért Máttyus, Namdar Homayounfar, Shrinidhi Kowshika Lakshmikanth, Andrei Pokrovsky, Raquel Urtasun (IROS 2019)
DAGMapper: Learning to Map by Discovering Lane Topology
Namdar Homayounfar, Wei-Chiu Ma*, Justin Liang*, Xinyu Wu, Jack Fan, Raquel Urtasun (ICCV 2019)
Convolutional Recurrent Network for Road Boundary Extraction
Justin Liang*, Namdar Homayounfar*, Wei-Chiu Ma, Shenlong Wang, Raquel Urtasun (CVPR 2019)
Deep Multi-Sensor Lane Detection
Min Bai*, Gellért Máttyus*, Namdar Homayounfar, Shenlong Wang, Shrinidhi Kowshika Lakshmikanth, Raquel Urtasun (IROS 2018, oral)
Hierarchical Recurrent Attention Networks for Structured Online Maps
Namdar Homayounfar, Wei-Chiu Ma, Shrinidhi Kowshika Lakshmikanth, Raquel Urtasun (CVPR 2018)
Sports Field Localization via Deep Structured Models
Namdar Homayounfar, Sanja Fidler, Raquel Urtasun (CVPR 2017)