• Publications
  • Influence
RegNet: Multimodal sensor registration using deep neural networks
TLDR
In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplified using a scanning LiDAR. Expand
  • 43
  • 10
  • PDF
Fully convolutional neural networks for dynamic object detection in grid maps
TLDR
We present a methods that uses a deep convolutional neural network (CNN) to infer whether grid cells are covering a moving object or not based on the structural appearance in the grid map. Expand
  • 14
  • 1
  • PDF
CNN-Based Lidar Point Cloud De-Noising in Adverse Weather
TLDR
We present a CNN-based approach for point cloud weather segmentation as an essential pre-processing step for lidar-based environment perception for autonomous vehicles and mobile robotics. Expand
  • 8
  • 1
  • PDF
Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation
TLDR
We present a novel deep neural network architecture called LiLaNet for point-wise, multi-class semantic labeling of semi-dense LiDAR data. Expand
  • 22
  • PDF
Improved Semantic Stixels via Multimodal Sensor Fusion
TLDR
This paper presents a compact and accurate representation of 3D scenes that are observed by a LiDAR sensor and a monocular camera. Expand
  • 5
  • PDF
Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling
TLDR
We propose a new CNN architecture for the point-wise semantic labeling of LiDAR data which achieves state-of-the-art results while increasing portability across sensor types. Expand
  • 1
  • PDF
LiDAR-based Semantic Labeling: Automotive 3D Scene Understanding
TLDR
In dieser Arbeit wird das neu entwickelte LiLaNet, eine echtzeitfahige, neuronale Netzarchitektur zur semantischen, punktweisen Klassifikation von LiDAR Punktwolken, vorgestellt, indem eine Klassenhierarchie in den Trainingsprozess integriert wird. Expand