Search Results for author:
Found 5 papers, 1 papers with code
Deep Parametric Continuous Convolutional Neural Networks
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)
Learning to Localize Using a LiDAR Intensity Map
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars.
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
The motion planners used in self-driving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules.
Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization
In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters.
SBNet: Sparse Blocks Network for Fast Inference
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.
