Johannes Kopp — accepted AI-conference papers

2024

Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions

Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp, Marc Walessa, Daniel Alexander Meißner, Klaus Dietmayer

RA-L 2024

Adverse weather conditions can severely affect the performance of LiDAR sensors by introducing unwanted noise in the measurements. Therefore, differentiating between noise and valid points is crucial for the reliable use of these sensors. Current approaches for detecting adverse weather points requi

Cited by 6 Source ↗Scholar ↗

2023

Energy-Based Detection of Adverse Weather Effects in LiDAR Data

Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp, Marc Walessa, Daniel Alexander Meissner, Klaus Dietmayer

RA-L 2023

Autonomous vehicles rely on LiDAR sensors to perceive the environment. Adverse weather conditions like rain, snow, and fog negatively affect these sensors, reducing their reliability by introducing unwanted noise in the measurements. In this work, we tackle this problem by proposing a novel approach

Cited by 26 Source ↗code ↗Scholar ↗

2023

Tackling Clutter in Radar Data - Label Generation and Detection Using PointNet++

Johannes Kopp, Dominik Kellner, Aldi Piroli, Klaus Dietmayer

ICRA 2023poster

Radar sensors employed for environment perception, e.g. in autonomous vehicles, output a lot of unwanted clutter. These points, for which no corresponding real objects exist, are a major source of errors in following processing steps like object detection or tracking. We therefore present two novel…

Cited by 16 Source ↗code ↗Scholar ↗