1 code implementation • CVPR 2022 • Julien Rebut, Arthur Ouaknine, Waqas Malik, Patrick Pérez
With their robustness to adverse weather conditions and ability to measure speeds, radar sensors have been part of the automotive landscape for more than two decades.
4 code implementations • ICCV 2021 • Arthur Ouaknine, Alasdair Newson, Patrick Pérez, Florence Tupin, Julien Rebut
Understanding the scene around the ego-vehicle is key to assisted and autonomous driving.
no code implementations • 25 Mar 2021 • Julien Rebut, Andrei Bursuc, Patrick Pérez
Robustness to various image corruptions, caused by changing weather conditions or sensor degradation and aging, is crucial for safety when such vehicles are deployed in the real world.
no code implementations • 4 Mar 2021 • Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Elnaz Jahani Heravi, Fahed Al Hassanat, Robert Laganiere, Julien Rebut, Waqas Malik
In this paper, we propose PolarNet, a deep neural model to process radar information in polar domain for open space segmentation.
no code implementations • 18 Mar 2020 • Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Fahed Al Hassanat, Elnaz Jahani Heravi, Robert Laganiere, Julien Rebut, Waqas Malik
In this work, we propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios.
no code implementations • 16 Jul 2019 • Farzan Erlik Nowruzi, Prince Kapoor, Dhanvin Kolhatkar, Fahed Al Hassanat, Robert Laganiere, Julien Rebut
In this paper, we take a comprehensive look into the effects of replacing real data with synthetic data.