Florian Piewak

According to our database, Florian Piewak authored at least 12 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation.

Mariella Dreissig, Florian Piewak, Joschka Boedecker

CoRR, 2024

2023

Survey on LiDAR Perception in Adverse Weather Conditions.

Mariella Dreissig, Dominik Scheuble, Florian Piewak, Joschka Boedecker

Proceedings of the IEEE Intelligent Vehicles Symposium, 2023

On the Calibration of Uncertainty Estimation in LiDAR-Based Semantic Segmentation.

Mariella Dreissig, Florian Piewak, Joschka Boedecker

Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023

2022

On the calibration of underrepresented classes in LiDAR-based semantic segmentation.

Mariella Dreissig, Florian Piewak, Joschka Boedecker

CoRR, 2022

2020

LiDAR-based Semantic Labeling: Automotive 3D Scene Understanding.

Florian Piewak

PhD thesis, 2020

CNN-Based Lidar Point Cloud De-Noising in Adverse Weather.

Robin Heinzler, Florian Piewak, Philipp Schindler, Wilhelm Stork

IEEE Robotics Autom. Lett., 2020

2019

Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling.

Florian Piewak, Peter Pinggera, Marius Zöllner

Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, 2019

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

Proceedings of the Computer Vision - ECCV 2018 Workshops, 2018

Improved Semantic Stixels via Multimodal Sensor Fusion.

Florian Piewak, Peter Pinggera, Markus Enzweiler, David Pfeiffer, Marius Zöllner

Proceedings of the Pattern Recognition - 40th German Conference, 2018

2017

Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps (Masters Thesis).

Florian Piewak

CoRR, 2017

RegNet: Multimodal sensor registration using deep neural networks.

Nick Schneider, Florian Piewak, Christoph Stiller, Uwe Franke

Proceedings of the IEEE Intelligent Vehicles Symposium, 2017

Fully convolutional neural networks for dynamic object detection in grid maps.

Florian Piewak, Timo Rehfeld, Michael Weber, Johann Marius Zöllner

Proceedings of the IEEE Intelligent Vehicles Symposium, 2017