5 citations · 6 across the 4 of their papers we have counts for
5 papers
LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds
Tobias Riedlinger, Marius Schubert, Sarina Penquitt +7
Object detection on Lidar point cloud data is a promising technology for autonomous driving and robotics which has seen a significant rise in performance and accuracy during recent…
Fake it, Mix it, Segment it: Bridging the Domain Gap Between Lidar Sensors
Frederik Hasecke, Pascal Colling, Anton Kummert
Segmentation of lidar data is a task that provides rich, point-wise information about the environment of robots or autonomous vehicles. Currently best performing neural networks fo…
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation
Pascal Colling, Matthias Rottmann, Lutz Roese-Koerner +1
We present a novel post-processing tool for semantic segmentation of LiDAR point cloud data, called LidarMetaSeg, which estimates the prediction quality segmentwise. For this purpo…
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps
Pascal Colling, Lutz Roese-Koerner, Hanno Gottschalk +1
We present a novel region based active learning method for semantic image segmentation, called MetaBox+. For acquisition, we train a meta regression model to estimate the segment-w…
Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities
Matthias Rottmann, Pascal Colling, Thomas-Paul Hack +4
We present a method that "meta" classifies whether seg-ments predicted by a semantic segmentation neural networkintersect with the ground truth. For this purpose, we employ measure…