218 citations · 221 across the 10 of their papers we have counts for
11 papers
Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation
Mariella Dreissig, Simon Ruehle, Florian Piewak +1
Safety-critical applications such as autonomous driving require robust 3D environment perception algorithms capable of handling diverse and ambiguous surroundings. The predictive p…
On the Calibration of Uncertainty Estimation in LiDAR-based Semantic Segmentation
Mariella Dreissig, Florian Piewak, Joschka Boedecker
The confidence calibration of deep learning-based perception models plays a crucial role in their reliability. Especially in the context of autonomous driving, downstream tasks lik…
Survey on LiDAR Perception in Adverse Weather Conditions
Mariella Dreissig, Dominik Scheuble, Florian Piewak +1
Autonomous vehicles rely on a variety of sensors to gather information about their surrounding. The vehicle's behavior is planned based on the environment perception, making its re…
On the calibration of underrepresented classes in LiDAR-based semantic segmentation
Mariella Dreissig, Florian Piewak, Joschka Boedecker
The calibration of deep learning-based perception models plays a crucial role in their reliability. Our work focuses on a class-wise evaluation of several model's confidence perfor…
CNN-based Lidar Point Cloud De-Noising in Adverse Weather
Robin Heinzler, Florian Piewak, Philipp Schindler +1
Lidar sensors are frequently used in environment perception for autonomous vehicles and mobile robotics to complement camera, radar, and ultrasonic sensors. Adverse weather conditi…
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…