4 papers
Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling
Florian Piewak, Peter Pinggera, Marius Zöllner
State-of-the-art approaches for the semantic labeling of LiDAR point clouds heavily rely on the use of deep Convolutional Neural Networks (CNNs). However, transferring network arch…
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps
Florian Piewak, Timo Rehfeld, Michael Weber +1
Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical appli…
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps (Masters Thesis)
Florian Piewak
One of the most important parts of environment perception is the detection of obstacles in the surrounding of the vehicle. To achieve that, several sensors like radars, LiDARs and…
RegNet: Multimodal Sensor Registration Using Deep Neural Networks
Nick Schneider, Florian Piewak, Christoph Stiller +1
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, exemplif…