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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)