no code implementations • 8 Aug 2019 • Wei-Chiu Ma, Ignacio Tartavull, Ioan Andrei Bârsan, Shenlong Wang, Min Bai, Gellert Mattyus, Namdar Homayounfar, Shrinidhi Kowshika Lakshmikanth, Andrei Pokrovsky, Raquel Urtasun
In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters.
no code implementations • 4 May 2019 • Min Bai, Gellert Mattyus, Namdar Homayounfar, Shenlong Wang, Shrinidhi Kowshika Lakshmikanth, Raquel Urtasun
Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving.
no code implementations • ICCV 2017 • Gellert Mattyus, Wenjie Luo, Raquel Urtasun
In contrast, in this paper we propose an approach that directly estimates road topology from aerial images.
no code implementations • ICCV 2017 • Shenlong Wang, Min Bai, Gellert Mattyus, Hang Chu, Wenjie Luo, Bin Yang, Justin Liang, Joel Cheverie, Sanja Fidler, Raquel Urtasun
In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712. 5 $km^2$ of land, 8439 $km$ of road and around 400, 000 buildings.
no code implementations • CVPR 2016 • Gellert Mattyus, Shenlong Wang, Sanja Fidler, Raquel Urtasun
In this paper we present an approach to enhance existing maps with fine grained segmentation categories such as parking spots and sidewalk, as well as the number and location of road lanes.
no code implementations • ICCV 2015 • Gellert Mattyus, Shenlong Wang, Sanja Fidler, Raquel Urtasun
In recent years, contextual models that exploit maps have been shown to be very effective for many recognition and localization tasks.