351 citations · 436 across the 5 of their papers we have counts for
5 papers
Deep Parametric Continuous Convolutional Neural Networks
Shenlong Wang, Simon Suo, Wei-Chiu Ma +2
Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks. This limits their applica…
Learning to Localize Using a LiDAR Intensity Map
Ioan Andrei Bârsan, Shenlong Wang, Andrei Pokrovsky +1
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity…
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
Abbas Sadat, Mengye Ren, Andrei Pokrovsky +3
The motion planners used in self-driving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules. This is usually achieved by two modules: beh…
Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization
Wei-Chiu Ma, Ignacio Tartavull, Ioan Andrei Bârsan +7
In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require d…
SBNet: Sparse Blocks Network for Fast Inference
Mengye Ren, Andrei Pokrovsky, Bin Yang +1
Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computatio…