no code implementations • CVPR 2018 • Shenlong Wang, Simon Suo, Wei-Chiu Ma, Andrei Pokrovsky, Raquel Urtasun
Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks.
Ranked #2 on Semantic Segmentation on S3DIS Area5 (Number of params metric)
no code implementations • 20 Dec 2020 • Ioan Andrei Bârsan, Shenlong Wang, Andrei Pokrovsky, Raquel Urtasun
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars.
no code implementations • 10 Oct 2019 • Abbas Sadat, Mengye Ren, Andrei Pokrovsky, Yen-Chen Lin, Ersin Yumer, Raquel Urtasun
The motion planners used in self-driving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules.
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.
2 code implementations • CVPR 2018 • Mengye Ren, Andrei Pokrovsky, Bin Yang, Raquel Urtasun
Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications.