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Fake it, Mix it, Segment it: Bridging the Domain Gap Between Lidar Sensors
We compare our method with two state of the art approaches for semantic lidar segmentation domain adaptation with a significant improvement for unsupervised and semi-supervised domain adaptation.
What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation
To train a well performing neural network for semantic segmentation, it is crucial to have a large dataset with available ground truth for the network to generalize on unseen data.
Fast Object Classification and Meaningful Data Representation of Segmented Lidar Instances
Object detection algorithms for Lidar data have seen numerous publications in recent years, reporting good results on dataset benchmarks oriented towards automotive requirements.
FLIC: Fast Lidar Image Clustering
Lidar sensors are widely used in various applications, ranging from scientific fields over industrial use to integration in consumer products.
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