Fully convolutional neural networks for dynamic object detection in grid maps. - Florian Piewak; Timo Rehfeld; Michael Weber 009; Johann Marius Zöllner (Intelligent Vehicles Symposium, 2017)

RegNet: Multimodal sensor registration using deep neural networks. - Nick Schneider; Florian Piewak; Christoph Stiller; Uwe Franke (Intelligent Vehicles Symposium, 2017)

Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps (Masters Thesis). - Florian Piewak (CoRR, 2017)

Improved Semantic Stixels via Multimodal Sensor Fusion. - Florian Piewak; Peter Pinggera; Markus Enzweiler; David Pfeiffer; Marius Zöllner 001 (GCPR, 2018)

Boosting LiDAR-Based Semantic Labeling by Cross-modal Training Data Generation. - Florian Piewak; Peter Pinggera; Manuel Schäfer; David Peter; Beate Schwarz; Nick Schneider; Markus Enzweiler; David Pfeiffer; J. Marius Zöllner (ECCV Workshops (6), 2018)

Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling. - Florian Piewak; Peter Pinggera; Marius Zöllner 001 (ITSC, 2019)

CNN-Based Lidar Point Cloud De-Noising in Adverse Weather. - Robin Heinzler; Florian Piewak; Philipp Schindler; Wilhelm Stork (IEEE Robotics Autom. Lett., 2020)

LiDAR-based Semantic Labeling: Automotive 3D Scene Understanding. - Florian Piewak (2020)

On the calibration of underrepresented classes in LiDAR-based semantic segmentation. - Mariella Dreissig; Florian Piewak; Joschka Boedecker (CoRR, 2022)

On the Calibration of Uncertainty Estimation in LiDAR-Based Semantic Segmentation. - Mariella Dreissig; Florian Piewak; Joschka Boedecker (ITSC, 2023)

Survey on LiDAR Perception in Adverse Weather Conditions. - Mariella Dreissig; Dominik Scheuble; Florian Piewak; Joschka Boedecker (IV, 2023)

Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation. - Mariella Dreissig; Florian Piewak; Joschka Boedecker (CoRR, 2024)